This PDF file contains the front matter associated with SPIE Proceedings Volume 10420 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Age is an inherent biological characteristic of human and is reflected in facial images to a certain extent. A method for estimating age from a facial image by combining CNN (Convolution Neural Network) with SVR (Support Vector Regression) is proposed. First, a deep CNN is trained to automatically extract age features from facial images and classify them into variant age groups. Then different SVRs are trained for each age group to estimate the age of a facial image. The experimental results show that a lower MAE (Mean Absolute Error) of age estimation on MORPH database is obtained.

The performance and robustness of fatigue detection largely decrease if the driver with glasses. To address this issue, this paper proposes a practical driver fatigue detection method based on face alignment at 3000 FPS algorithm. Firstly, the eye regions of the driver are localized by exploiting 6 landmarks surrounding each eye. Secondly, the HOG features of the extracted eye regions are calculated and put into SVM classifier to recognize the eye state. Finally, the value of PERCLOS is calculated to determine whether the driver is drowsy or not. An alarm will be generated if the eye is closed for a specified period of time. The accuracy and real-time on testing videos with different drivers demonstrate that the proposed algorithm is robust and obtain better accuracy for driver fatigue detection compared with some previous method.

Compensating the illumination of a face image is an important process to achieve effective face recognition under severe illumination conditions. This paper present a novel illumination normalization method which specifically considers removing the illumination boundaries as well as reducing the regional illumination. We begin with the analysis of the commonly used reflectance model and then expatiate the hybrid usage of adaptive non-local smoothing and the local information coding based on Weber’s law. The effectiveness and advantages of this combination are evidenced visually and experimentally. Results on Extended YaleB database show its better performance than several other famous methods.

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

The Web application presented here supports plant production and works with the graph database Neo4j shell to support the assessment of the condition of crops on the basis of geospatial data, including raster and vector data. The adoption of a graph database as a tool to store and manage the data, including geospatial data, is completely justified in the case of those agricultural holdings that have a wide range of types and sizes of crops. In addition, the authors tested the option of using the technology of Microsoft Cognitive Services at the level of produced application that enables an image analysis using the services provided. The presented application was designed using ASP.NET MVC technology and a wide range of leading IT tools.

In the universal computerization era important aspect is the possibility of man partial replacement in the identification process of the disease cases occurring in the plantations cultivation. This is very important when it comes to the process of early response to the disease and at a later stage for early treatment to help stop the disease or its elimination, which has its economic justification. Presented in this paper system may be the answer to these issues in the production of sugar beet

In order to deal with the insufficiency of recently algorithms based on Two Dimensions Fractional Fourier Transform (2D-FrFT), this paper proposes a multiple order features based method for emotion recognition. Most existing methods utilize the feature of single order or a couple of orders of 2D-FrFT. However, different orders of 2D-FrFT have different contributions on the feature extraction of emotion recognition. Combination of these features can enhance the performance of an emotion recognition system. The proposed approach obtains numerous features that extracted in different orders of 2D-FrFT in the directions of x-axis and y-axis, and uses the statistical magnitudes as the final feature vectors for recognition. The Support Vector Machine (SVM) is utilized for the classification and RML Emotion database and Cohn-Kanade (CK) database are used for the experiment. The experimental results demonstrate the effectiveness of the proposed method.

Human gait, as a soft biometric, helps to recognize people by walking. To further improve the recognition performance under cross-view condition, we propose Joint Bayesian to model the view variance. We evaluated our prosed method with the largest population (OULP) dataset which makes our result reliable in a statically way. As a result, we confirmed our proposed method significantly outperformed state-of-the-art approaches for both identification and verification tasks. Finally, sensitivity analysis on the number of training subjects was conducted, we find Joint Bayesian could achieve competitive results even with a small subset of training subjects (100 subjects). For further comparison, experimental results, learning models, and test codes are available.

In complex scene, the exact determination and division of color is the key to color flag recognition tasks. Aiming at the problem that the traditional recognition methods have low recognition accuracy and can not be identified in complex scene and multi-targets, this paper proposes a color threshold determination (CTD) method to identify color flag. Firstly, the color sample data set under different light conditions is constructed, and the filtering of the noise pixels is realized by the projection method in the HSV color space to obtain the final color decision threshold. Secondly, color decision is made for each pixel in the candidate region of interest detected by Gentle Adaboost cascade classifier based on HOG feature. Finally, the color feature is matched by the preset threshold, and the matching region is retained to obtain the final recognition results. Experiments show that the proposed approach has better performance in the color flag recognition task under complex scene. The recognition accuracy was 97.1%, the sensitivity was 90.70% and the specificity was 99.33%.

In this paper, we present a technique to automatically identify some typical radio frequency interference from pulsar surveys using support vector machine. The technique has been tested by candidates. In these experiments, to get features of SVM, we use principal component analysis for mosaic plots and its classification accuracy is 96.9%; while we use mathematical morphology operation for smog plots and horizontal stripes plots and its classification accuracy is 86%. The technique is simple, high accurate and useful.

The reconstruction of 3D objects from 2D line drawings is regarded as one of the key topics in the field of computer vision. The ongoing research is mainly focusing on the reconstruction of 3D objects that are mapped only from 2D straight lines, and that are symmetric in nature. Commonly, this approach only produces basic and simple shapes that are mostly flat or rather polygonized in nature, which is normally attributed to inability to handle curves. To overcome the above-mentioned limitations, a technique capable of handling non-symmetric drawings that encompass curves is considered. This paper discusses a novel technique that can be used to reconstruct 3D objects containing curved lines. In addition, it highlights an application that has been developed in accordance with the suggested technique that can convert a freehand sketch to a 3D shape using a mobile phone.

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

The order of cigarette market is a key issue in the tobacco business system. The anti-counterfeiting code, as a kind of effective anti-counterfeiting technology, can identify counterfeit goods, and effectively maintain the normal order of market and consumers' rights and interests. There are complex backgrounds, light interference and other problems in the anti-counterfeiting code images obtained by the tobacco recognizer. To solve these problems, the paper proposes a locating method based on Susan operator, combined with sliding window and line scanning,. In order to reduce the interference of background and noise, we extract the red component of the image and convert the color image into gray image. For the confusing characters, recognition results correction based on the template matching method has been adopted to improve the recognition rate. In this method, the anti-counterfeiting code can be located and recognized correctly in the image with complex background. The experiment results show the effectiveness and feasibility of the approach.

The goal of sign language recognition (SLR) is to translate the sign language into text, and provide a convenient tool for the communication between the deaf-mute and the ordinary. In this paper, we formulate an appropriate model based on convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) network, in order to accomplish the continuous recognition work. With the strong ability of CNN, the information of pictures captured from Chinese sign language (CSL) videos can be learned and transformed into vector. Since the video can be regarded as an ordered sequence of frames, LSTM model is employed to connect with the fully-connected layer of CNN. As a recurrent neural network (RNN), it is suitable for sequence learning tasks with the capability of recognizing patterns defined by temporal distance. Compared with traditional RNN, LSTM has performed better on storing and accessing information. We evaluate this method on our self-built dataset including 40 daily vocabularies. The experimental results show that the recognition method with CNN-LSTM can achieve a high recognition rate with small training sets, which will meet the needs of real-time SLR system.

Flag is a rather special recognition target in image recognition because of its non-rigid features with the location, scale and rotation characteristics. The location change can be handled well by the depth learning algorithm Convolutional Neural Networks (CNNs), but the scale and rotation changes are quite a challenge for CNNs. Since it has good rotation and gray scale invariance, the local binary pattern (LBP) is combined with grayscale stretching and CNNs to make LBP and grayscale stretching as CNNs pretreatment, which can not only significantly improve the efficiency of flag recognition, but can also evaluate the recognition effect through ROC, accuracy, MSE and quality factor.

With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user’s location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

In this paper, we propose a novel approach based on Depth Maps and compute Multi-Scale Histograms of Oriented Gradient (MSHOG) from sequences of depth maps to recognize actions. Each depth frame in a depth video sequence is projected onto three orthogonal Cartesian planes. Under each projection view, the absolute difference between two consecutive projected maps is accumulated through a depth video sequence to form a Depth Map, which is called Depth Motion Trail Images (DMTI). The MSHOG is then computed from the Depth Maps for the representation of an action. In addition, we apply L2-Regularized Collaborative Representation (L2-CRC) to classify actions. We evaluate the proposed approach on MSR Action3D dataset and MSRGesture3D dataset. Promising experimental result demonstrates the effectiveness of our proposed method.

In order to solve the problem of the subtle inter-class difference and large intra-class variation caused by different poses in fine grained object recognition, a new algorithm is proposed for fine-grained object recognition based on pose alignment and part based representation. In the training stage, firstly, dozens of typical poses of the object are summed up by clustering, and then a pose classifier is trained based on them which can be used to align object pose. Then, the fine-grained classifiers are trained by the visible parts and global feature ensembles in each pose subclass. Given an unseen test image, the probabilities that current test image belongs to each pose subclass are gotten via the pose classifier. The output from first k subclass classifiers is weighted combined to form a single classification decision. Our approach is especially valuable for fine-grained recognition when intra-class variations are extremely high. The proposed method is validated by the experimental results on the bird database CUB_2010-2011, which has the large intra-class variations. The results show that the proposed method has better performance than the existing methods.

Multi-object tracking is particularly challenging in many scenarios with similar appearance and frequent occlusions among targets. In this paper, we present an online detection-based multi-object tracking method. In each frame, kernerlized convolution filter are adopted to track isolated and un-occluded targets. To overcoming fixed scale in KCF, trackers are associated with detection responses. If a target is associated with a detection, then the target size is updated by the average of this detection size and the previous estimated size. When occlusions are detected, the multiple interaction among targets is formulated as an optimization problem and we explore two-layer hierarchical Particle Swarm Optimization algorithm for the optimal solution. The first layer is designed for the superficial targets which is visible. The second layer is designed for the bottom occluded targets which can guided by first visible layer and we propose to incorporate the attractive force into the particle evolution process. Experimental results on public datasets demonstrate that our proposed method alleviating drifting problem and effectively reduces ID switches and lost trajectories.

Multi-object tracking in semi-crowded environment is a difficult task. Traditional methods relying only on visual cues, like appearance and simple motion prediction fails because of frequent occlusions. In this paper, a new method based on formation stability is proposed to learn the interaction information among pedestrians. whatever the relationship of a pedestrian with others is, it will remain unchanged for a while. That is, when a pedestrian is occluded, the relationship with others will stay the same after the reappearance of the pedestrian. So trajectory clips caused by occlusions can be associated together. Effectiveness of this method is validated by experiments.

In this paper, a novel target tracking algorithm based on spatio-temporal context is proposed. During the tracking process, the camera shaking or occlusion may lead to the failure of tracking. The proposed algorithm can solve this problem effectively. The method use the spatio-temporal context algorithm as the main research object. We get the first frame’s target region via mouse. Then the spatio-temporal context algorithm is used to get the tracking targets of the sequence of frames. During this process a similarity measure function based on perceptual hash algorithm is used to judge the tracking results. If tracking failed, reset the initial value of Mean Shift algorithm for the subsequent target tracking. Experiment results show that the proposed algorithm can achieve real-time and stable tracking when camera shaking or target occlusion.

In order to achieve real-time target tracking and locating for UAV, a reliable processing system is built on the embedded platform. Firstly, the video image is acquired in real time by the photovoltaic system on the UAV. When the target information is known, KCF tracking algorithm is adopted to track the target. Then, the servo is controlled to rotate with the target, when the target is in the center of the image, the laser ranging module is opened to obtain the distance between the UAV and the target. Finally, to combine with UAV flight parameters obtained by BeiDou navigation system, through the target location algorithm to calculate the geodetic coordinates of the target. The results show that the system is stable for real-time tracking of targets and positioning.

Traditional wavelet threshold denoising algorithm is insufficient for the preservation of edge detail information and the separation of noise. In this paper, proposed the optimization of wavelet threshold denoising method based on edge detection, which to process the edge image obtained by the wavelet edge detection algorithm, and fuse the smoothing image produced by the improved threshold function model. Experimental results show that compared with the traditional wavelet threshold denoising method, this algorithm effectively preserves the edge information of the image and remove the noise, also improved signal to noise ratio obviously

A surface defect detection method based on multi-features is proposed for the detection of complex ceramic disc surface. Firstly, geometric transformation is implemented on the image being detected, which makes the direction and position of the image to be unified. Then, according to the standard template, the image is separated into several regions with different gray characteristics for interval inspect. Defects containing broken, chipping, uneven polishing, crack, etc. are classified into two types—blob defect and crack defect. Blob defect detection is mainly based on the analysis of the abnormal part of regional gray value, and the crack detection is based on the analysis of shape and neighborhood gray gradient changes. Experimental result shows that the algorithm proposed is sensitive to the surface defects, and can effectively reduce the false positives with detection rate more than 94%, detection speed up to 5 parts per second.

In response to the slow running speed of the Deformable Part Model algorithm in the process of the pedestrian detection, this paper incorporated Cascade Detection algorithm and Branch-and-Bound algorithm into a fast pedestrian detection algorithm which is based on Deformable Part Model. In a pedestrian detection process, a sequence model evaluates individual parts sequentially to quickly prune most of the smaller possible objects. This aims to accelerate the process of object positioning, and to optimize global classification results in all possible image regions. Meanwhile, the boundaries of the maximum are adopted to search the clipping operation of the window. In order to improve detection speed without compromising the accuracy of the detection, this paper increase the number of the part models involved. According to the experimental results on INRIA data set, the proposed algorithm successfully improved the accuracy and the speed of detection.

Object detection is a computer vision problem which caught a large amount of attention. But the candidate boundingboxes extracted from only image features may end up with false-detection due to the semantic gap between the top-down and the bottom up information. In this paper, we propose a novel method for generating object bounding-boxes proposals using the combination of eye fixation point, saliency detection and edges. The new method obtains a fixation orientated Gaussian map, optimizes the map through single-layer cellular automata, and derives bounding-boxes from the optimized map on three levels. Then we score the boxes by combining all the information above, and choose the box with the highest score to be the final box. We perform an evaluation of our method by comparing with previous state-ofthe art approaches on the challenging POET datasets, the images of which are chosen from PASCAL VOC 2012. Our method outperforms them on small scale objects while comparable to them in general.

Automatic inspection takes a great role in guaranteeing the product quality. But one of the limitations of current inspection algorithms is either product specific or problem specific. In this paper, we propose a defect detection method based on three image features fusion for variety of industrial products surface detection. The proposed method learns sub-image gray level difference, color histogram and pixel regularity of qualified images off-line and test the images based on the detection results of these three image features. It avoids the feature training of defect products as it is difficult to collect large amount of defect samples. The experimental results show that the detection accuracy is between 93% and 98% and the approach is efficient for the real time applications of industrial product inspect.

In this paper, we propose a novel cascade detection algorithm which focuses on point and line defects on TFT-LCD. At the first step of the algorithm, we use the gray level difference of su-bimage to segment the abnormal area. The second step is based on phase only transform (POT) which corresponds to the Discrete Fourier Transform (DFT), normalized by the magnitude. It can remove regularities like texture and noise. After that, we improve the method of setting regions of interest (ROI) with the method of edge segmentation and polar transformation. The algorithm has outstanding performance in both computation speed and accuracy. It can solve most of the defect detections including dark point, light point, dark line, etc.

The change point detection in periodic time series is much desirable in many practical usages. We present a novel algorithm for this task, which includes two phases: 1) anomaly measure- on the basis of a typical regression model, we propose a new computation method to measure anomalies in time series which does not require any reference data from other measurement(s); 2) change detection- we introduce a new martingale test for detection which can be operated in an unsupervised and nonparametric way. We have conducted extensive experiments to systematically test our algorithm. The results make us believe that our algorithm can be directly applicable in many real-world change-point-detection applications.

Indoor Autonomous Vehicle(IAV) is used in many indoor scenes. Such as hotels and hospitals. Door detection is a key issue to guide the IAV into rooms. In this paper, we consider door detection in the use of indoor navigation of IAV. Since real-time properties are important for real-world IAV, the detection algorithm must be fast enough. Most monocular-camera based door detection model need a perfect detection of the four line segments of the door or the four corners. But in many situations, line segments could be extended or cut off. And there could be many false detected corners. And few of them can distinguish doors from door-like objects with door-like shape effectively. We proposed a 2-D vision model of the door that is made up of line segments. The number of parts detected is used to determine the possibility of a door. Our algorithm is tested on a database of doors.1 The robustness and real-time are verified. The precision is 89.4%. Average time consumed for processing a 640x320 figure is 44.73ms.

This paper introduces a joint feature of Fourier histograms of oriented gradients (FHOG) and local binary pattern (LBP) for vehicle detection in aerial images. Both of them are rotation invariant, so any rotation angle of vehicle in aerial images can be easily detected. A linear support vector machine (SVM) classifier is then trained over the joint feature vectors for the final vehicle detection. We evaluate our method on a public dataset and compare with some state-of-theart algorithms, the proposed joint feature outperforms them in detecting small targets in complicated backgrounds.

Infrared small target detection is one of the vital techniques in infrared search and track surveillance systems. An efficient method based on target-background separation via local morphological component analysis (MCA) sparse representation is proposed in this paper. This method converts infrared small target detection problem over entire image into target-background separation over image patches according to the different morphological component between target and background. An adaptive dictionary is trained adaptively by K-singular value decomposition (K-SVD) according to infrared image, and then the dictionary is subdivided by a total-variation-like activity measure into two categories: the target component dictionary explaining target signal and background component dictionary embedding background. Finally, the interest target can be easily extracted through threshold segmentation in target component image constructed by target dictionary. The experimental results demonstrate the effectiveness of the proposed method.

Offshore accurate replenishment technology is the foundation for conducting ocean research for every country. However, it is difficult to keep the security and accuracy of hoisting due to the fact that waves will let vessels generate 6-DOF motions. This paper regards accurate offshore supplying as a background, and takes into consideration that vessel acts as a rigid body to perform algorithm research on the AHC (Active Heaving Compensation) detection system. The binocular camera installed on the hoisting equipment can calculate the 6-DOF pose of vessel via detecting landmarks on the deck. The system can achieve all-weather operations, adopting Shi-Tomasi algorithm to identify and L-K Optical Flow algorithm to track sub-pixel points. Lastly, the scheme has been verified in the 6-DOF motion platform, which indicates that its accuracy meets the requirements of the control experiment in the next step.

A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.

Considering that traditional image edge detection methods can’t reflect the orientation selection characteristics of a human visual perception system and the firing mechanism of neuron spikes, a new method of image edge detection based on orientation response mechanisms of an integrate-and-fire (IF) neurons model are presented in this paper. To fully reflect orientation selection characteristics of the human visual system, the Log-Gabor orientation response model is applied for image orientation preprocessing. The spikes sequence fired by IF neurons model is used for image edge detection. The results of various experiments show that our method can truly reflect the biological characteristics. By comparison with the traditional image edge detection methods, our method is focused on the fine details preservation, and can highlight image edge.

Random Pulse Repetition Interval Compressed Sensing Radar (RPRICSR) has superiorities on target detection, unambiguous velocity measurement as well as anti-velocity jamming for its signal randomness. However, RPRICSR cannot detect the micro motion targets effectively under the presence of micro motion false targets within present echo processing methods. This paper firstly combined Short Sparse Recovery (SSR) method with RPRI signal under the frame of Compressed Sensing. Then the fact that echo signals of true and false micro motion targets are sparse in different dictionaries are utilized to separate the true and false micro motion targets based on a union dictionary. The proposed method is proved to be effective compared to the traditional signal processing methods of RPRICSR according to the simulation results.

For intelligent vehicle surveillance systems, it is a big challenge to detect small, blurred car plates of vehicles driving on a highway. In this paper, we present a novel, two-stage detection scheme for small, blurred car-plate detection in large surveillance images. Our proposed scheme firstly detects vehicles, and then locates the car plates in specific regions of detected vehicles based on our proposed car-face landmark localization algorithm. Our scheme can also solve the high false-alarming rate problem with small, blurred car-plate detection. Experimental results show that our proposed method is accurate, and able to reduce the false-alarming rate, without any compromise in speed.

Moving target detection in image sequence for dynamic scene is an important research topic in the field of computer vision. Block projection and matching are utilized for global motion estimation. Then, the background image is compensated applying the estimated motion parameters so as to stabilize the image sequence. Consequently, background subtraction is employed in the stabilized image sequence to extract moving targets. Finally, divide the difference image into uniform grids and average optical flow is employed for motion analysis. Experiment tests show that the proposed average optical flow method can efficiently extract the vehicle targets from dynamic scene meanwhile decreasing the false alarm.

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

Structured light 3D vision inspection is widely used in various 3D surface reconstruction techniques. However, there are still two problems to be solved. The first problem is that if the relative position between the camera and laser projector is changed, the system has to be calibrated again. The second problem is that a turntable or a linear positioning system is needed in order to obtain the complete 3D reconstruction data in conventional methods. To overcome these problems, we proposed a novel design using a dual semicircular planar target and a rotating laser projector to perform calibration and 3D reconstruction simultaneously. The system contains two semicircular planar targets hollowed out at center which are installed in up and down position. The middle part of the light stripe can pass through the blank part of semicircular planar target and then cast on the target object, while the rest of the light stripe will cast on the semicircular planar target. The laser projector is rotated 360 degrees along an axis perpendicular to the planar target plane to get the complete 3D surface reconstruction of the target object. Experiments are conducted to validate the flexibility and accuracy of the system.

Human vision system possesses a strong ability to interpret complex scenes in real time effortlessly and efficiently. However, computational modeling of basic intelligent behavior still remains a significant challenge in the fields of computer vision system. Different from context-aware saliency algorithm, our method for saliency detection, not only using basic properties of visual attention but also introducing local patches contrast cues, thus it can effectively improve the accuracy of visual saliency detection with relatively low computational complexity. Furthermore, the experimental results also indicate that the effectiveness of our proposed method.

In order to achieve a single-frame enhancement and detection of dim point targets efficiently, a new method based on morphological top-hat transformation and modified non-local means (NL-means, NLM) for target enhancement is presented. After enhancing dim point targets, an estimating algorithm called local reverse entropy is applied to get candidate targets, and obtains detecting results in the end. In this model, white top-hat and black top-hat are combined to obtain pre-processed image, then the targets are enhanced for the second time through modified NL-means algorithm. The background in the residual image of enhanced image and original image is mostly suppressed, then it is regarded as the input of local reverse entropy estimation method (LREM). Detecting results can be obtained by setting proper thresholds. The 4 × 4 weak lattice targets with different brightness are superimposed on different infrared image backgrounds. The experimental results show that, when the SCR is low (SCR≈1), the detection algorithm model proposed in this paper has higher SCR gain than other target-enhancing algorithms such as TDLMS, max-median, max-mean, non-local means, etc. and the detection performance is the best.

In this paper, we propose a novel approach to detect visual saliency based on improved graph models. The graph-based manifold ranking algorithm adopts k neighbor constraint and close-loop constraint to construct a graph model. In the first stage of the algorithm, k = 2 neighbor constraint reduces the differences between foreground nodes and boundary nodes and the saliency value of the nodes which are close to the center of the image tends to be larger. So some background nodes are selected as foreground queries in the second stage and some background will be highlighted. To eliminate such defect of the algorithm, we use a simple graph structure containing close-loop constraint in the first stage and update the graph structure by expanding close-loop constraint to calculate a new ranking matrix, which is then used to calculate the final saliency map. Experiments in the MSRA1000 database prove that our method outperforms the classical algorithm.

Since the study of fuel consumptions is of great importance and the related data is accessible, many researches about factors affecting fuel consumptions have appeared.To sum up, the driving style of drivers, automobile emissions and the type of the routes are the mainly three factors.Classification is relatively single.In order to improve the classification accuracy’ this text studies that there exist some special roads,when drivers drive through them,their fuel consumptions will be similar because of road qualities.To achieve this goal, the first step is to calculate fuel consumptions per 100km of all city roads.Recognizing and examining special roads based on the ST-Matching algorithm.Third is to analyze road qualities of special roads like the length of roads, the speed of driving, and compare them with other common roads.Then we choose 4 cases to analyze. We find that when driving through special roads, fuel consumptions would be similar because of road qualities.Besides, the average length of special roads is longer than common roads’ and the mean velocity, the speed of getting and off special roads are faster than common roads’.The findings in this paper can filter out special road segments as noisy data in the study of relationship between driving styles and fuel consumptions,and it also has very high practical significance on recommending fuel-efficient paths.

The visual detection technology plays an important role in the industry of precision casting. With pertinence to the features of crack images of precision casting, an automatic detection and identification software for cracks of precision casting integrating crack segmentation, identification, measurements and extractions was designed and developed. Adopting the threshold segmentation, morphological processing, feature identifications as well as feature extraction and proposing a detection method with pertinence to the precision casting crack characteristics. The software effectively eliminates the influences on the detection results by the operators due to such factors as the detection environment, fatigue degree, visual resolution and working experience. The processing of the actually collected crack images of precision casting verified the efficacy and practicality of the software in the field of casting detection.

In this paper, a novel approach to change detection in synthetic aperture radar (SAR) images based on structure similarity (SSIM) and parametric kernel graph cuts is presented. Firstly, the SSIM is imported into change detection and a difference image constructed method based on SSIM is proposed. And then, the changed and unchanged pixels are identified from the difference image by the parametric kernel graph cuts algorithm. Experimental results obtained on real SAR images demonstrate the effectiveness of the proposed method.

The generation of 3D models for indoor objects and scenes is an attractive tool for digital city, virtual reality and SLAM purposes. Panoramic images are becoming increasingly more common in such applications due to their advantages to capture the complete environment in one single image with large field of view. The extraction and matching of image feature points are important and difficult steps in three-dimensional reconstruction, and ASIFT is a state-of-the-art algorithm to implement these functions. Compared with the SIFT algorithm, more feature points can be generated and the matching accuracy of ASIFT algorithm is higher, even for the panoramic images with obvious distortions. However, the algorithm is really time-consuming because of complex operations and performs not very well for some indoor scenes under poor light or without rich textures. To solve this problem, this paper proposes an improved ASIFT algorithm for indoor panoramic images: firstly, the panoramic images are projected into multiple normal perspective images. Secondly, the original ASIFT algorithm is simplified from the affine transformation of tilt and rotation with the images to the only tilt affine transformation. Finally, the results are re-projected to the panoramic image space. Experiments in different environments show that this method can not only ensure the precision of feature points extraction and matching, but also greatly reduce the computing time.

Many stereo matching algorithms use fixed color thresholds and a rigid cross skeleton to segment supports (viz., Cross method), which, however, does not work well for different images. To address this issue, this paper proposes a novel dual adaptive support (viz., DAS)-based stereo matching method, which uses both appearance and shape information of a local region to segment supports automatically, and, then, integrates the DAS-based cost aggregation with the absolute difference plus census transform cost, scanline optimization and disparity refinement to develop a stereo matching system. The performance of the DAS method is also evaluated in the Middlebury benchmark and by comparing with the Cross method. The results show that the average error for the DAS method 25.06% lower than that for the Cross method, indicating that the proposed method is more accurate, with fewer parameters and suitable for parallel computing.

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry – the authors’ previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

Intelligent computer image processing technology provides convenience and possibility for designers to carry out designs. Shape analysis can be achieved by extracting SURF feature. However, high dimension of SURF feature causes to lower matching speed. To solve this problem, this paper proposed a fast fabric image matching algorithm based on SURF K-means and LSH algorithm. By constructing the bag of visual words on K-Means algorithm, and forming feature histogram of each image, the dimension of SURF feature is reduced at the first step. Then with the help of LSH algorithm, the features are encoded and the dimension is further reduced. In addition, the indexes of each image and each class of image are created, and the number of matching images is decreased by LSH hash bucket. Experiments on fabric image database show that this algorithm can speed up the matching and retrieval process, the result can satisfy the requirement of dress designers with accuracy and speed.

Gabor filters and K-means algorithm are two commonly used texture analysis methods. However, the texture feature vector has a high dimension by using Gabor filters, which will influence the operating efficiency. Meanwhile, K-means algorithm is affected by the initial clustering centers, and it may lead to the decrease of classification accuracy. Hence, Relief algorithm is applied to make a feature selection for Gabor texture feature, and obtain a suitable texture feature sunset. Furthermore, cuckoo search is used to optimize the clustering center of K-means algorithm, and enhance the accuracy and efficiency of texture recognition. Experimental results demonstrate the effectiveness of the proposed method.

An accurate measurement of large aviation part plays a key role in the assembly of aircraft. However, due to the limitation of spatial size, a calibration with large field of view and an accurate surface measurement of large part is hard to achieve. In this paper, an improved measurement method with spatial constraint calibration method and feature compression extraction method is proposed. Firstly, based on the proposed spatial constraint calibration method, the vision system is conveniently and precisely calibrated by using the designed SBA and SLT. Images of scanning laser stripes are captured by the calibrated cameras, simultaneously. Then the proposed feature compression extraction method is adopted to accurately extract centers of laser stripes. Finally, based on the binocular vision principle, the surface of part is reconstructed. The accuracy of proposed calibration method is verified in the lab. The results of the measurement of a standard part show the validity and precision of the proposed method.

The robust and distinctive local feature description is a critical component of image matching. In this paper, a novel descriptor, DGOH descriptor, is presented based on dual gradient orientation histogram. DGOH is a full rotation invariant descriptor, which takes advantage of spatial information of features by utilizing intensity order based subregion division method, meanwhile adopts the rotation invariant gradient calculation method in descriptor construction process. The discriminability of DGOH descriptor is enhanced by utilizing dominant and secondary gradient orientation histogram to represent the detected affine feature region. Performance evaluation experiments are carried out on the standard Oxford dataset. The experimental results show that the DGOH descriptor outperforms the state-of-the-art descriptors in terms of stability, precision and efficiency.

Feature extraction plays a key role in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very crucial to choose appropriate features to train a classifier, which is prerequisite. Inspired by the great success of Bag-of-Visual-Words (BoVW), we address the problem of feature extraction by proposing a novel feature extraction method for SAR target classification. First, Gabor based features are adopted to extract features from the training SAR images. Second, a discriminative codebook is generated using K-means clustering algorithm. Third, after feature encoding by computing the closest Euclidian distance, the targets are represented by new robust bag of features. Finally, for target classification, support vector machine (SVM) is used as a baseline classifier. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release dataset are conducted, and the classification accuracy and time complexity results demonstrate that the proposed method outperforms the state-of-the-art methods.

This paper proposes a visual perceptual descriptor (VPD) and a new approach to extract perceptual depth feature for 2D image retrieval. VPD mimics human visual system, which can easily distinguish regions that have different textures, whereas for regions which have similar textures, color features are needed for further differentiation. We apply VPD on the gradient direction map of an image, capture texture-similar regions to generate a VPD map. We then impose the VPD map on a quantized color map and extract color features only from the overlapped regions. To reflect the nature of perceptual distance in single 2D image, we propose and extract the perceptual depth feature by computing the nuclear norm of the sparse depth map of an image. Extracted color features and the perceptual depth feature are both incorporated to a feature vector, we utilize this vector to represent an image and measure similarity. We observe that the proposed VPD + depth method achieves a promising result, and extensive experiments prove that it outperforms other typical methods on 2D image retrieval.

To bridge the gap between the fuzziness of biometrics and the exactitude of cryptography, based on combining palmprint with two-layer error correction codes, a novel biometrics encryption method is proposed. Firstly, the randomly generated original keys are encoded by convolutional and cyclic two-layer coding. The first layer uses a convolution code to correct burst errors. The second layer uses cyclic code to correct random errors. Then, the palmprint features are extracted from the palmprint images. Next, they are fused together by XORing operation. The information is stored in a smart card. Finally, the original keys extraction process is the information in the smart card XOR the user’s palmprint features and then decoded with convolutional and cyclic two-layer code. The experimental results and security analysis show that it can recover the original keys completely. The proposed method is more secure than a single password factor, and has higher accuracy than a single biometric factor.