Yoichi Tomioka

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.

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.

Collaborative patrol planning of mobile surveillance cameras for perfect observation of moving objects

Patrolling by mobile robots is an attractive option for enhancing the reliability of video surveillance systems; mobile robots can observe a wide area effectively during particular intervals, which helps in the early detection of fires and other unusual situations. Moreover, if mobile robots patrol so that anymoving objects cannot exist without being observed, such patrol can be helpful in detecting suspicious individuals. In this paper, we propose a method for determining the minimum number of mobile surveillance cameras and their patrol plans that can realize periodic observation of all regions and moving objects. We demonstrate that we can obtain short patrol courses to achieve reliablemonitoring in our experiments.

Enhanced patrol course planning method for multiple mobile surveillance cameras

Video surveillance systems are becoming increasingly important for crime investigation and deterrence. By the rapid advance of mobile robot technologies, mobile surveillance cameras are becoming an attractive option for the video surveillance systems. In this paper, we propose a method for obtaining the minimum number of mobile surveillance cameras and their shortest patrol courses under the following two conditions. First, the restriction of the visibility must be taken into consideration. Second, each region must be observed at a certain interval. In our experiments, we demonstrate that effective patrol courses for mobile surveillance cameras can be generated.

An FPGA implementation of 3D numerical simulations on a 2D SIMD array processor

Three-dimensional (3D) numerical simulation is an indispensable technique for various analyses of physical phenomena, but it generally requires numerous computation. In this paper, we propose an FPGA-based accelerator for 3D numerical simulations and focus on acceleration of the 3D finite-difference time-domain (FDTD) method. This accelerator consists of a 2D single instruction multiple data (SIMD) array processor, and it can execute 3D parallel computing with little data transfer overhead by applying virtual processing-elements cuboid (VPEC) with synchronous shift data transfer. We demonstrate that the experimental hardware implemented on an Altera Stratix V FPGA (5SGSMD5K2F40C2N) is 3.1 times faster than parallel computing on the NVIDIA Tesla C2075, and it reaches a 94.57% operating rate of the calculation units for the computation of the 3D FDTD method. The proposed accelerator is suitable for multi-chip composition.

Travelling route of mobile surveillance camera

A video surveillance system is becoming more and more important for investigation and deterrent of crimes, and cameras installed in public space are increasing. However, a number of cameras is required to observe a wide and complex area with cameras installed at fixed positions. In order to efficiently observe a wide and complex area at lower cost, mobile robots have attracted attention. In this paper, we propose a method to plan a travelling route of mobile surveillance camera. Our method is based on mixed integer linear programming, and obtains an optimum travelling route such that a camera with certain visual angle and distance can observe all regions at the shortest intervals. Our method is applied to artificially generated data with various parameters, and it is shown that the shortest route which is more appropriate for the observation is obtained compared to a TSP-based method.

Lithography hotspot detection by two-stage cascade classifier using histogram of oriented light propagation

In advanced semiconductor-process technology, the ability to detect and repair lithography hotspots, which can affect printability, is essential. In this paper, we propose a two-stage cascade classifier for accurate hotspot detection. Our classifier uses a novel layout feature based on the propagation of light passing through a photomask. We performed experiments to evaluate our cascade classifier by applying it to the ICCAD-2012 CAD contest problem. The hotspot detection performance was evaluated according to two indices: (I1) the number of correctly detected hotspots over the number of actual hotspots and (I2) the number of correctly detected hotspots over the number of false hotspots. The results showed that the proposed method gained a 1.15% improvement in I1 and 24.4 times improvement in I2 on average compared to existing state-of-the-art methods, even the one with the best I1.

A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification

In recent years, camera identification methods have attracted attention in the field of digital forensics. The existing camera identification methods use features, such as the Exif header data and image noise, that indicate the characteristics of the camera. Of them, photo-response non-uniformity (PRNU) noise contains the unique features of an image sensor and is different for each individual camera. A camera identification method using the PRNU noise should have high identification ability, and a camera identification method using the pairwise magnitude relations of the clustered PRNU noise was previously proposed. In general, identification accuracy is estimated from test data sets, such as the Dresden image database. However, identification accuracy can be evaluated only with respect to the range of images within a database in the conventional evaluation method. A more detailed accuracy evaluation method is required for practical use. Furthermore, studies have not yet reported a false acceptance rate (FAR) evaluation method for the clustered PRNU pair-based camera identification capable of guaranteeing a low FAR (e.g.,

An FPGA Implementation of Multi-stream Tracking Hardware using 2D SIMD Array (Abstract Only)

\rm {FAR}=10^{-9}