In this paper, a smart contrast enhancement technique based on conventional histogram equalization (HE) algorithm is proposed. This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go though a repartitioning test to ensure the absence of any dominating portions. This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts.

A Dynamic Histogram Equalization for Image Contrast Enhancement

Image inpainting refers to the process of restoring missing or damaged areas in an image. This field of research has been very active over recent years, boosted by numerous applications: restoring images from scratches or text overlays, loss concealment in a context of impaired image transmission, object removal in a context of editing, or disocclusion in image-based rendering (IBR) of viewpoints different from those captured by the cameras. Although earlier work dealing with disocclusion has been published in [1], the term inpainting first appeared in [2] by analogy with a process used in art restoration.

Image Inpainting : Overview and Recent Advances

A review of smart home applications based on Internet of Things

MPEG-4 is the multimedia standard for combining interactivity, natural and synthetic digital video, audio and computer-graphics Typical applications are: internet, video conferencing, mobile videophones, multimedia cooperative work, teleteaching and games With MPEG-4 the next step from block-based video (ISO/IEC MPEG-1, MPEG-2, CCITT H261, ITU-T H263) to arbitrarily-shaped visual objects is taken This significant step demands a new methodology for system analysis and design to meet the considerably higher flexibility of MPEG-4 Motion estimation is a central part of MPEG-1/2/4 and H261/H263 video compression standards and has attracted much attention in research and industry, for the following reasons: it is computationally the most demanding algorithm of a video encoder (about 60-80% of the total computation time), it has a high impact on the visual quality of a video encoder, and it is not standardized, thus being open to competition Algorithms, Complexity Analysis, and VLSI Architectures for MPEG-4 Motion Estimation covers in detail every single step in the design of a MPEG-1/2/4 or H261/H263 compliant video encoder: Fast motion estimation algorithms Complexity analysis tools Detailed complexity analysis of a software implementation of MPEG-4 video Complexity and visual quality analysis of fast motion estimation algorithms within MPEG-4 Design space on motion estimation VLSI architectures Detailed VLSI design examples of (1) a high throughput and (2) a low-power MPEG-4 motion estimator Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation is an important introduction to numerous algorithmic, architectural and system design aspects of the multimedia standard MPEG-4 As such, all researchers, students and practitioners working in image processing, video coding or system and VLSI design will find this book of interest

Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation

Neural Networks (NN) are a family of models for a broad range of emerging machine learning and pattern recondition applications. NN techniques are conventionally executed on general-purpose processors (such as CPU and GPGPU), which are usually not energy-efficient since they invest excessive hardware resources to flexibly support various workloads. Consequently, application-specific hardware accelerators for neural networks have been proposed recently to improve the energy-efficiency. However, such accelerators were designed for a small set of NN techniques sharing similar computational patterns, and they adopt complex and informative instructions (control signals) directly corresponding to high-level functional blocks of an NN (such as layers), or even an NN as a whole. Although straightforward and easy-to-implement for a limited set of similar NN techniques, the lack of agility in the instruction set prevents such accelerator designs from supporting a variety of different NN techniques with sufficient flexibility and efficiency.In this paper, we propose a novel domain-specific Instruction Set Architecture (ISA) for NN accelerators, called Cambricon, which is a load-store architecture that integrates scalar, vector, matrix, logical, data transfer, and control instructions, based on a comprehensive analysis of existing NN techniques. Our evaluation over a total of ten representative yet distinct NN techniques have demonstrated that Cambricon exhibits strong descriptive capacity over a broad range of NN techniques, and provides higher code density than general-purpose ISAs such as ×86, MIPS, and GPGPU. Compared to the latest state-of-the-art NN accelerator design DaDianNao [5] (which can only accommodate 3 types of NN techniques), our Cambricon-based accelerator prototype implemented in TSMC 65nm technology incurs only negligible latency/power/area overheads, with a versatile coverage of 10 different NN benchmarks.

Cambricon: an instruction set architecture for neural networks

This work presents a novel algorithm using color contrast enhancement and lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings. Color contrast enhancement based on saturation and de-saturation is performed in the u'v'Y color space, to change the saturation value in the chromaticity diagram, and adaptive histogram equalization then is adopted to adjust the luminance component. Additionally, this work presents a new patching method using the Markov random field (MRF) model of texture synthesis. Eliminating undesirable aged painting patterns, such as stains, crevices, and artifacts, and then filling the lacuna regions with the appropriate textures is simple and efficient. The synthesization procedure integrates three key approaches, weighted mask, annular scan and auxiliary, with neighborhood searching. These approaches can maintain a complete shape and prevent edge disconnection in the final results. Moreover, the boundary between original and synthesized paintings is seamless, and we are unable to distinguish in which the undesirable pattern appears.

Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis

A novel image recovery algorithm for removing visible watermarks is presented. Independent component analysis (ICA) is utilized to separate source images from watermarked and reference images. Three independent component analysis approaches are examined in the proposed algorithm, which includes joint approximate diagonalization of eigenmatrices, second-order blind identification, and FastICA. Moreover, five different visible watermarking methods to embed uniform and linear-gradient watermarks are implemented. The experimental results show that visible watermarks are successfully removed, and that the proposed algorithm is independent of both the adopted ICA approach and the visible watermarking method. In the final experiment, several public domain images sourced from various websites are tested. The results of this study demonstrate that the proposed algorithm can blindly and successfully remove the visible watermarks without knowing the watermarking methods in advance

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A Novel Image Recovery Algorithm for Visible Watermarked Images

The high development of medicine causes the world's population aging quickly. To resolve the problem with limited medical resources, constant monitoring of elders' activity of daily living is important. We propose an activity recognition system for smart home, so elders can live alone and their children can monitor their parents' living activity to achieve the concept of "Aging in Place". The living activity monitoring model is powerful to recognize meaningful activities by using both ambient and wearable sensors. It's feasible to deploy in the real living environment be-cause it's a non-parametric learning model. Elders need less effort to label activity in training part, and the model may have chance to find some special activities that the elders did not consider in the past. We demonstrate the living activity monitoring model is feasible to be deployed in a living home with high accuracy performance of the activity recognition result.

Monitoring Elder's Living Activity Using Ambient and Body Sensor Network in Smart Home

Nowadays, close monitoring of daily activities of elders is enabled by employment of the advanced wireless sensor networks, whose large quantity data are then analyzed by activity recognition techniques whereby their behavior patterns can be accurately modelled. In general, behavior patterns are important information about how elders live, and caregivers can thus take care of elders easily with the help from that informaiton. So far, there are many research results related to learning of human behavior; however, their assumptions are usually either too simple or inflexible to account for complex human behaviors in real life, which change dynamically depending on contexts. We here present a context-aware framework for human behavior learning and prediction. Such framework discovers contexts from resident's real life data and adapts correspoinding behavior patterns next accordingly. We evaluate the framework on two public datasets, and the experimental results show promising results.

Context-aware activity prediction using human behavior pattern in real smart home environments

This paper presents a modified approach to the successive mean quantization transform, which is called as the weighted histogram separation (WHS) for enhancement of color images. Property of WHS situates between the successive mean quantization transform and the histogram equalization. In addition, this approach is further applied to the local enhancement, which is similar to the adaptive histogram equalization, and it is termed as the adaptive weighted histogram separation (AWHS). A comparison with successive mean quantization transform and histogram equalization has been performed in the experiments.

Yi-Chong Zeng

Papers

Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis

A Novel Image Recovery Algorithm for Visible Watermarked Images

Monitoring Elder's Living Activity Using Ambient and Body Sensor Network in Smart Home

Context-aware activity prediction using human behavior pattern in real smart home environments

Color Images Enhancement using Weighted Histogram Separation