This paper presents an advanced video camera system which has robust automatic focus (AF), automatic exposure (AE), and automatic white-balance (AWB) control. The proposed AF algorithm decides the correct moving direction of the lens and detects the accurate in-focus state even though the scene is interfered with by the high light intensity. Experimental results demonstrate that the proposed system can be an effective alternative to conventional systems using the hill-climbing method.

An advanced video camera system with robust AF, AE, and AWB control

This chapter presents an introduction to the different artistic design domains that make use of interactive evolutionary design approaches, the techniques they use, and many of the challenges arising. After a brief introduction to concepts and terminology common to most artificial genetic design, there is a survey of artistic evolutionary systems and related research for evolving images and forms. While the focus is primarily on purely aesthetic fitness landscapes, the survey also ventures into areas such as product design and architecture. The overview shifts from technique to application as organizational strategies, as appropriate. After briefly surveying additional information sources, the chapter concludes with a discussion of major topics of relevance to evolutionary system designers, providing context for the following chapters. It is hoped that this snapshot of the state of the field will increase exposure to projects and issues, discussion amongst participants, and ultimately the accessibility of these techniques and approaches.

Evolutionary Visual Art and Design

A new multiagent system (MAS) design for fault location, isolation, and restoration in power distribution systems (PDSs) is presented. When there is a fault in the PDS, MAS quickly isolates the fault and restores the service to the fault-free zones. Hierarchical coordination strategy is introduced to manage the agents, which integrate the advantages of both centralized and decentralized coordination strategies. Proposed MAS is composed of zone agents (ZAs), feeder agents (FAs), and substation agents. In this framework, ZA locate and isolate the fault based on the locally available information, and assist the FA for reconfiguration and restoration. FA can solve the restoration problem using the existing algorithms for the 0-1 knapsack problem. A novel ${Q}$ -learning mechanism is also introduced to support the FA in decision making for restoration. The design is illustrated by the use of case studies of fault location, isolation, and restoration on West Virginia super circuit. The results from the case studies indicate the performance of proposed MAS design.

A Multi-Agent Design for Power Distribution Systems Automation

We review camera architecture in the age of artificial intelligence. Modern cameras use physical components and software to capture, compress and display image data. Over the past 5 years, deep learning solutions have become superior to traditional algorithms for each of these functions. Deep learning enables 10-100x reduction in electrical sensor power per pixel, 10x improvement in depth of field and dynamic range and 10-100x improvement in image pixel count. Deep learning enables multiframe and multiaperture solutions that fundamentally shift the goals of physical camera design. Here we review the state of the art of deep learning in camera operations and consider the impact of AI on the physical design of cameras.

Smart Cameras

The proposed algorithm provides fast and accurate auto-exposure capability for digital still cameras. For normal lighting conditions, the number of preview frames and the exposure error are within 3.5 frames and 3.92%. For high contrast lighting conditions, the number of preview frames and the exposure error are within 8.8 frames and 6.56%. Furthermore, it offers accurate detection for both back lit and excessive front lit conditions at the same time and make proper exposure to the main object.

An Auto-exposure algorithm for detecting high contrast lighting conditions

The ink drop spread (IDS) method is a modeling technique developed by algorithmically mimicking the information-handling processes of the human brain. This method has been proposed as a new approach to soft computing. IDS modeling is characterized by processing that uses intuitive pattern information instead of complex formulas, and it is capable of stable and fast convergences. This paper investigates the modeling ability of the IDS method based on three typical benchmarks. Experimental results demonstrated that the IDS method can handle various modeling targets, ranging from logic operations to complex nonlinear systems, and that its modeling performance is satisfactory in comparison with that of feedforward neural networks.

A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing

Auto exposure (AE) is an important function of video cameras to adjust the image luminance. In this paper, an exposure control system of the AE using color information is discussed. Current AE systems detect special image conditions such as backlighting and excessive frontlighting in which the luminance of a main object deteriorates, and compensate the exposure in order to obtain the appropriate luminance of the main object. The compensation is determined according to the degree of backlighting and excessive frontlighting, regardless of the the background. The exposure control system proposed in this paper uses "hue" and "chroma" of pixels to derive the importance of the background, and determines the amount of compensation required by fuzzy reasoning. Simulations of AE were carried out using both the conventional system and proposed method. Results showed that the proposed system was more efficient for AE than the conventional method.

An exposure control system of video cameras based on fuzzy logic using color information

The ink drop spread (IDS) method is a modeling technique used in the active learning method (ALM), which is a new approach to soft computing. It is characterized by a modeling process which is based on computing that uses intuitive pattern information instead of complex formulas. It has been proved that the IDS method is capable of stable fast modeling for complex nonlinear targets. In this paper, the classification performance of the IDS method is investigated. The two-spiral problem is a popular classification benchmark, and it is difficult to achieve the perfect classification due to high nonlinearity. With regard to this benchmark the IDS method exhibited good performance in terms of the classification rate and learning speed. This paper also present two learning modes, one of which is effective in solving the two-spiral problem rapidly.

Classification performance of the IDS method based on the two-spiral benchmark

An Exposure Control System of Video Cameras Based on Fuzzy Logic Using Color Information

This paper presents a hardware unit for modeling systems using the active learning method (ALM). The ALM, a new methodology of soft computing, has processing units called IDS's, which are tasked with extracting useful information from a system subject to modeling. In realizing the ALM in hardware, it is desirable in terms of processing nature, performance, and scalability to utilize dedicated hardware for IDS. A developed high performance IDS processing unit enables ALM-based modeling systems to increase real-time capabilities. The hardware implementation of IDS and performance test results of the hardware are reported, and a consideration of the redundancy of IDS processing units is described.

Masayuki Murakami

Papers

A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing

An exposure control system of video cameras based on fuzzy logic using color information

Classification performance of the IDS method based on the two-spiral benchmark

An Exposure Control System of Video Cameras Based on Fuzzy Logic Using Color Information

A high performance IDS processing unit for a new fuzzy-based modeling