Optic disc is one of the main features in retinal images. Its location plays an important role for automated screening systems. A novel approach for automatic optic disc location is proposed in this paper. It consists of the following three stages, in the first stage, preprocessing is applied for correcting the uneven illumination and improving the low contrast. In the second stage, a series of key points can be extracted using alternative sequential filters and regional maxima. In the third stage, the key point that has the maximum correlation coefficient calculated by adaptive multi-scale template matching method is located as the center of the optic disc. The proposed algorithm is tested on two publicly available databases including DRIVE and DIRATEDB1. And our experiment results indicate that the proposed algorithm gives better performance than the state-of-the-art approaches.
Image recognition is of great significance currently, especially the one based on RGB images. Recently, as a substantial amount of RGB based CNNs showing great performance on image recognition problems, the initialization problem sees an increasingly importance as is highly associated with the variance as well as the convergence effect. Even though there exists several prevailing initialization methods, they didn’t consider the difference of contributions from different RGB channels on image recognition. Thus, in this paper, we put forward a proper approach to take the RGB contribution into account. Firstly, we conduct experiments to prove that RGB channels have respectively different contributions upon RGB based image recognition by the means that training one general CNN and three CNNs under datasets seperated from CIFAR-10 by RGB channel. Then, the ensemble of those CNNs shows how similar performance as the general one and also their respective accuracies are recorded as the influence proportion, namely their contribution ratio. As long as the influence proportion is obtained, according to that, we can initialize the weights of convolution filters in order to improve the effect of initialization.
Abnormal event detection in crowded scenes is a challenging task in the computer vision community. A hybrid motion descriptor named the multiscale histogram of first- and second-order motion is proposed for abnormal event detection. The second-order motion describes the change in motion and is extracted by optical flow-based instantaneous tracking, which avoids object tracking in crowded scenes. For the modeling of normal events, a kernel null Foley–Sammon transform (KNFST) is introduced. KNFST makes a projection in the null space, where normal samples of all types are treated jointly instead of considering each known class individually. Experiments conducted on two benchmark datasets and comparisons to state-of-the-art methods demonstrate the superiority of the proposed method.
In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.
Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.
Abnormal event detection plays a critical role for intelligent video surveillance, and detection in crowded scenes is a challenging but more practical task. We present an abnormal event detection method for crowded video. Region-wise modeling is proposed to address the inconsistent detected motion of the same object due to different depths of field. Comparing to traditional block-wise modeling, the region-wise method not only can reduce heavily the number of models to be built but also can enrich the samples for training the normal events model. In order to reduce the computational burden and make the region-based anomaly detection feasible, a saliency detection technique is adopted in this paper. By identifying the salient parts of the image sequences, the irrelevant blocks are ignored, which removes the disturbance and improves the detection performance further. Experiments on the benchmark dataset and comparisons with the state-of-the-art algorithms validate the advantages of the proposed method.