Based on the cellular neural network (CNN) paradigm, the bio-inspired (bi-i) cellular vision system is a computing platform consisting of state-of-the-art sensing, cellular sensing-processing and digital signal processing. This paper presents the implementation of a novel CNN-based segmentation algorithm onto the bi-i system. The experimental results, carried out for different benchmark video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frame/sec. Comparisons with existing CNN-based methods show that, even though these methods are from two to six times faster than the proposed one, the conceived approach is more accurate and, consequently, represents a satisfying trade-off between real-time requirements and accuracy.
Segmentation is the process of representing a digital image into multiple meaningful regions. Since these applications
require more computational power in real time applications, we have implemented a new segmentation algorithm using
the capabilities of Eye-RIS Vision System to execute the algorithm in very short time. The segmentation algorithm is
implemented mainly in three steps. In the first step, which is pre-processing step, the images are acquired and noise
filtering through Gaussian function is performed. In the second step, Sobel operators based edge detection approach is
implemented on the system. In the last step, morphologic and logic operations are used to segment the images as post
processing. The experimental results performed for different images show the accuracy of the proposed segmentation
algorithm. Visual inspection and timing analysis (7.83 ms, 127 frame/sec) prove that the proposed segmentation
algorithm can be executed for real time video processing applications. Also, these results prove the capability of Eye-RIS
Vision System for real time image processing applications
Proc. SPIE. 7245, Image Processing: Algorithms and Systems VII
KEYWORDS: MATLAB, Edge detection, Digital signal processing, Detection and tracking algorithms, Image processing, Computer programming, Signal processing, Neural networks, Image enhancement, Analog electronics
Bi-i (Bio-inspired) Cellular Vision system is built mainly on Cellular Neural /Nonlinear Networks (CNNs) type
(ACE16k) and Digital Signal Processing (DSP) type microprocessors. CNN theory proposed by Chua has advanced
properties for image processing applications. In this study, the edge detection algorithms are implemented on the Bi-i
Cellular Vision System. Extracting the edge of an image to be processed correctly and fast is of crucial importance for
image processing applications. Threshold Gradient based edge detection algorithm is implemented using ACE16k
microprocessor. In addition, pre-processing operation is realized by using an image enhancement technique based on
Laplacian operator. Finally, morphologic operations are performed as post processing operations. Sobel edge detection
algorithm is performed by convolving sobel operators with the image in the DSP. The performances of the edge
detection algorithms are compared using visual inspection and timing analysis. Experimental results show that the
ACE16k has great computational power and Bi-i Cellular Vision System is very qualified to apply image processing
algorithms in real time.
Image segmentation is an important and difficult computer vision problem. Hyper-spectral images pose even more
difficulty due to their high-dimensionality. Spectral clustering (SC) is a recently popular clustering/segmentation
algorithm. In general, SC lifts the data to a high dimensional space, also known as the kernel trick, then derive
eigenvectors in this new space, and finally using these new dimensions partition the data into clusters. We demonstrate
that SC works efficiently when combined with covariance descriptors that can be used to assess pixelwise similarities
rather than in the high-dimensional Euclidean space. We present the formulations and some preliminary results of the
proposed hybrid image segmentation method for hyper-spectral images.
In many production systems, the products are inspected by human operators who observe faults with their naked eye
while most of the other manufacturing activities are automated. However, manual inspection is slow and yields
subjective results. To defeat this problem, image processing based visual control systems have been integrated to the
production systems. The visual system performance depends on the robustness of the image processing techniques.
Especially, the thresholding technique plays crucial role if you are inspecting scratches on the products. Since utilizing
the constant threshold fails in many cases, we have proposed an adaptive thresholding technique based visual inspection
system to detect production faults rapidly and efficiently without hampering the manufacturing process. The proposed
visual system also includes rotation invariant properties, which is important to get high speed processing.