Hitoshi Kitazawa

Optimum camera placement considering camera specification for security monitoring

We present an optimum camera placement algorithm. We are motivated by the fact that the installation of security cameras is increasing rapidly. From the system cost point of view, it is desirable to observe all the area of interest by the smallest number of cameras. We propose a method for deciding optimum camera placement automatically considering camera specification such as visual distance, visual angle, and resolution. Moreover, to reduce the number of cameras, we divide the scene into regions and weight them according to their importance of observation. Then, we calculate camera locations which cover all the important regions and as many other regions as possible by considering the trade off between a number of observed regions and the number of cameras required. We formulate this problem as a set covering problem.

Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise

Owing to the rapid progress in digital camera technologies, a large amount of image content is distributed on the World Wide Web. Digital camera identification, which is the identification of the source camera of an input image, is becoming increasingly important for presenting evidence in a court and helping police investigations. In recent years, a digital camera identification method using the image sensors pattern noise has received considerable attention. Photo-response non-uniformity (PRNU) noise is mainly generated by the existence of differences between the sensitivities of pixels, and it is useful as a fingerprint of a camera. However, the PRNU noise of an image is usually contaminated by random noise and scene content and affected by the image processing engine, which inhibits stable identification. In this paper, we propose a novel digital camera identification method using the pairwise magnitude relations of image sensor noise, which are robust to noise contamination. By performing experiments, we demonstrate that the proposed method can identify the source cameras of query images with high accuracy.

An improved camera identification method based on the texture complexity and the image restoration

The identification of source camera is useful to improve the capability of evidence in the digital image such as distinguish the photographer taking illegal images and adopting digital images as evidence of crime. Lukáš, et al. showed the method for source camera identification based on the correlation of PNU (pixel nonuniformity) noise. However, the wavelet-based denoising filter for suppressing the random noise reduces the accuracy of camera identification. It is caused by the fact that the denoising filter diffuses the edge and makes the PNU noise less pronounced. Moreover, it is difficult to extract PNU noise from the images taken by cameras which are equipped with the image improvement functions such as motion blur correction, contrast enhancement, and noise reduction. In this paper, we propose a method for improving the camera identification accuracy by selecting pixels based on the texture complexity. We also propose a-method for improving the identification accuracy by applying the image restoration method.

A new concept of security camera monitoring with privacy protection by masking moving objects

We present a novel framework for encoding images obtained by a security monitoring camera with protecting the privacy of moving objects in the images. We are motivated by the fact that although security monitoring cameras can deter crimes, they may infringe the privacy of those who and objects which are recorded by the cameras. Moving objects, whose privacy should be protected, in an input image (recorded by a monitoring camera) are encrypted and hidden in a JPEG bitstream. Therefore, a normal JPEG viewer generates a masked image, where the moving objects are unrecognizable or completely invisible. Only a special viewer with a password can reconstruct the original recording. Data hiding is achieved by watermarking and encrypting with the advanced encryption standard (AES). We illustrate a concept of our framework and an algorithm of the encoder and the special viewer. Moreover, we show an implementation example.

Post-layout optimization of power and timing for ECL LSIs

An optimization algorithm for power and timing of bipolar ECL LSls is proposed. The power dissipation is minimized by a nonlinear programming solver under accurate timing constraints extracted from layout. The power and delay time of an ECL gate are considered functions of its switching current which is regulated by programming its resistors. Experimental results show significant power reductions for circuits including a real chip without degrading the performance.<<ETX>>

Generation of an Optimum Patrol Course for Mobile Surveillance Camera

Video surveillance systems are becoming increasingly important for crime investigation and deterrence, and the number of cameras installed in public space is increasing. However, many cameras installed at fixed positions are required to observe a wide and complex area. In order to efficiently observe such a wide area at lower cost, mobile robots are an attractive option. In this paper, we propose a method for determining the traveling route of a mobile surveillance camera. Our method is based on mixed integer linear programming and obtains an optimum traveling route such that a camera with a certain visual angle and visual distance can observe the entire region at the shortest intervals. Through our experiments, we apply this method to several artificially generated data and data for a real university campus and demonstrate that effective patrol courses for specified mobile surveillance cameras can be generated.

Digital camera identification based on the clustered pattern noise of image sensors

Along with the popularization of digital cameras, the reliable identification of digital image source is becoming increasingly important as an evidence in a court and some help of investigations. In this paper, we propose an enhanced digital camera identification method using the pixel non-uniformity (PNU) noise of image sensors. By clustering the PNU noises, the proposed method extracts the robust features of image sensors to the random noise, scene content, and image processing engine such as the noise reduction. In the experiments, the proposed method shows the high identification accuracy even for latest digital cameras.

Representation of Pseudo Inter-reflection and Transparency by Considering Characteristics of Human Vision

We have succeeded in developing a quick and fully automated system that can generate photo‐realistic 3D CG data based on a real object. A major factor in this success comes from our findings through psychophysical experiments that human observers do not have an accurate idea of what should be actually reflected as inter‐reflections on the surface of an object. Taking advantage of this characteristic of human vision, we propose a new inter‐reflection representation technique in which inter‐reflections are simulated by allowing the same quantity of reflection components as there are in the background to pass through the object. Since inter‐reflection and transparency are calculated by the same algorithm, our system can capture 3D CG data from various real objects having a strong inter‐reflection, such as plastic and porcelain items or translucent glass and acrylic resin objects. The synthetic images from the 3D CG data generated with this pseudo inter‐reflection and transparency look very natural. In addition, the 3D CG data and synthetic images are produced quickly at a lower cost.

A Global Router Optimizing Timing and Area for High-Speed Bipolar LSI's

A timing-driven global routing algorithm applicable to high-speed bipolar LSIs is proposed. Path-based timing constraints are directly modeled and routing paths are selected using novel heuristic criteria to minimize area as well as to satisfy the timing constraints by keeping track of the critical path delay and channel densities. Using bipolarspecific features, this router can be applied to Gbit/s LSIs. Experimental results shows that the average delay improvement was 17.6% and the algorithm is quite promising.

A multistage dataflow implementation of a Deep Convolutional Neural Network based on FPGA for high-speed object recognition

Deep Neural Networks (DNNs) have progressed significantly in recent years. Novel DNN methods allow tasks such as image and speech recognition to be conducted easily and efficiently, compared with previous methods that needed to search for valid feature values or algorithms. However, DNN computations typically consume a significant amount of time and high-performance computing resources. To facilitate high-speed object recognition, this article introduces a Deep Convolutional Neural Network (DCNN) accelerator based on a field-programmable gate array (FPGA). Our hardware takes full advantage of the characteristics of convolutional calculation; this allowed us to implement all DCNN layers, from image input to classification, in a single chip. In particular, the dateflow from input to classification is uninterrupted and paralleled. As a result, our implementation achieved a speed of 409.62 giga-operations per second (GOPS), which is approximately twice as fast as the latest reported result. Furthermore, we used the same architecture to implement a Recurrent Convolutional Neural Network (RCNN), which can, in theory, provide better recognition accuracy.