A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction.

This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network) that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

High-Performance Motion Estimation for Image Sensors with Video Compression.

It is important to reduce the time cost of video compression for image sensors in video sensor network. Motion estimation (ME) is the most time-consuming part in video compression. Previous work on ME exploited intra-frame data reuse in a reference frame to improve the time efficiency but neglected inter-frame data reuse. We propose a novel inter-frame data reuse scheme which can exploit both intra-frame and inter-frame data reuse for ME in video compression (VC-ME). Pixels of reconstructed frames are kept on-chip until they are used by the next current frame to avoid off-chip memory access. On-chip buffers with smart schedules of data access are designed to perform the new data reuse scheme. Three levels of the proposed inter-frame data reuse scheme are presented and analyzed. They give different choices with tradeoff between off-chip bandwidth requirement and on-chip memory size. All three levels have better data reuse efficiency than their intra-frame counterparts, so off-chip memory traffic is reduced effectively. Comparing the new inter-frame data reuse scheme with the traditional intra-frame data reuse scheme, the memory traffic can be reduced by 50% for VC-ME.

A Novel 2D-to-3D Video Conversion Method Using Time-Coherent Depth Maps.

In this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market. Generally, 2D image sensor and 2D-to-3D conversion chip can compose a 3D image sensor. Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor. In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input. Global depth gradient is computed according to image type while local depth refinement is related to color information. As input 2D video content consists of a number of video shots, the proposed algorithm reuses the global depth gradient of frames within the same video shot to generate time-coherent depth maps. The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.

Fast traffic sign recognition with a rotation invariant binary pattern based feature.

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

A multi-modal face recognition method using complete local derivative patterns and depth maps.

In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features.