Efficient hardware implementations of low bit depth motion estimation algorithms

In this paper, we present efficient hardware implementation of multiplication free one-bit transform (MF1BT) based and constraint one-bit transform (C-1BT) based motion estimation (ME) algorithms, in order to provide low bit-depth representation based full search block ME hardware for real-time video encoding. We used a source pixel based linear array (SPBLA) hardware architecture for low bit depth ME for the first time in the literature. The proposed SPBLA based implementation results in a genuine data flow scheme which significantly reduces the number of data reads from the current block memory, which in turn reduces the power consumption by at least 50% compared to conventional 1BT based ME hardware architecture presented in the literature. Because of the binary nature of low bit-depth ME algorithms, their hardware architectures are more efficient than existing 8 bits/pixel representation based ME architectures

Full depth CNN classifier for handwritten and license plate characters recognition

Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional layers, two max-pooling layers, and one fully connected layer. This architecture is low-complex, fast, reliable and achieves very promising classification accuracy that may move the field forward in terms of low complexity, high accuracy and full feature extraction. The proposed approach is tested on four benchmarks for handwritten character datasets, Fashion-MNIST dataset, public LP character datasets and a newly introduced real LP isolated character dataset. The proposed approach tests report an error of only 0.28% for MNIST, 0.34% for MAHDB, 1.45% for AHCD, 3.81% for AIA9K, 5.00% for Fashion-MNIST, 0.26% for Saudi license plate character and 0.97% for Latin license plate characters datasets. The license plate characters include license plates from Turkey (TR), Europe (EU), USA, United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA)

High resolution 3-D face modeling and model warping

In this work, 3-D representations of human faces are obtained at a computer by making use of a DLP projector and a high resolution camera. The system calibration is carried out using a calibration pattern image. A colored structured light approach is adopted in this paper instead of the traditional gray-tone pattern to obtain depth information. A heuristic color correction approach is proposed in this work to improve the performance of the previous approaches. Experiments show that high resolution depth information can be extracted with this approach. Furthermore, a GUI is designed to enable user controlled modifications of the 3-D model

An all binary sub-pixel motion estimation approach and its hardware architecture

Motion estimation (ME) is the most computationally intensive part of a video coding system. Therefore it is very important to reduce its computational complexity. In this paper, a novel all-binary approach for reducing the computational complexity of sub-pixel accurate ME is proposed. An efficient hardware architecture for the proposed all-binary sub-pixel accurate motion estimation approach is also presented. The proposed hardware architecture has significantly low hardware complexity and therefore very low power consumption. It can process 720p video frames at 30 fps in a pipelined fashion together with the integer ME hardware. Therefore, it can be used in real-time low power video coding systems required by many mobile consumer electronics devices