The Camera sensor network is a new advent of technology in which each sensor node can capture video signal, process and communicate with other nodes. We have investigated a dense node configuration. The requested processing task in this network is arbitrary view generation among nodes view. To avoid unnecessary communication between nodes in this network and to speed up the processing time, we propose a distributed processing architecture where the number of nodes sharing image data are optimized. Therefore, each sensor node processes part of the interpolation algorithm with local communication between sensor nodes. Two processing methods are used based on the image size shared. These two methods are F-DP (Fully image shared Distributed Processing) and P-DP (Partially image shared Distributed Processing). In this research, the network processing time has been theoretically analyzed for one user. The theoretical results are compatible with the experimental results. In addition, the performance of proposed DP methods were compared with Centralized Processing (CP). As a result, the best processing method for optimum number of nodes can be chosen based on (i) communication delay of the network, (ii) whether the network has one or more channels for communication among nodes and (iii) the processing ability of nodes.

In this paper, we present an automatic vision-based traffic sign recognition system, which can detect and classify traffic signs at long distance under different lighting conditions. To realize this purpose, the traffic sign recognition is developed in an originally proposed dual-focal active camera system. In this system, a telephoto camera is equipped as an assistant of a wide angle camera. The telephoto camera can capture a high accuracy image for an object of interest in the view field of the wide angle camera. The image from the telephoto camera provides enough information for recognition when the accuracy of traffic sign is low from the wide angle camera. In the proposed system, the traffic sign detection and classification are processed separately for different images from the wide angle camera and telephoto camera. Besides, in order to detect traffic sign from complex background in different lighting conditions, we propose a type of color transformation which is invariant to light changing. This color transformation is conducted to highlight the pattern of traffic signs by reducing the complexity of background. Based on the color transformation, a multi-resolution detector with cascade mode is trained and used to locate traffic signs at low resolution in the image from the wide angle camera. After detection, the system actively captures a high accuracy image of each detected traffic sign by controlling the direction and exposure time of the telephoto camera based on the information from the wide angle camera. Moreover, in classification, a hierarchical classifier is constructed and used to recognize the detected traffic signs in the high accuracy image from the telephoto camera. Finally, based on the proposed system, a set of experiments in the domain of traffic sign recognition is presented. The experimental results demonstrate that the proposed system can effectively recognize traffic signs at low resolution in different lighting conditions.

We show that distributed source coding of multi-view images in camera sensor networks (CSNs) using adaptive modules can come close to the Slepian-Wolf bound. In a systematic scenario with limited node abilities, work by Slepian and Wolf suggest that it is possible to encode statistically dependent signals in a distributed manner to the same rate as with a system where the signals are jointly encoded. We considered three nodes (PN, CN and CNs), which are statistically depended. Different distributed architecture solutions are proposed based on a parent node and child node framework. A PN sends the whole image whereas a CNs/CN only partially, using an adaptive coding based on adaptive module-operation at a rate close to theoretical bound - H(CNs|PN)/H(CN|PN,CNs). CNs sends sub-sampled image and encodes the rest of image, however CN encodes all image. In other words, the proposed scheme allows independent encoding and jointly decoding of views. Experimental results show performance close to the information-theoretic limit. Furthermore, good performance of the proposed architecture with adaptive scheme shows significant improvement over previous work.