Guest Editorial: Machine Vision Applications

Yasuyo Kita, Hiroshi Ishikawa, Takeshi Masuda
2017 International Journal of Computer Vision  
Machine vision, system-oriented and application-oriented subarea of computer vision, has drastically progressed over the last few decades along with the progress of computer vision theories and been playing the key roles in our daily life. In addition, the computational environment, such as massive progress of computational power and the accumulation of common big data, as well as the ubiquity of 2D and 3D cameras, enables machine vision systems to deal with a wide range of real world problems.
more » ... Along this line of progress, we have decided to edit this special issue, machine vision applications, by collecting ten representative papers in the field in order to show the current and future directions in the field to the IJCV audience. Parts/system inspection has a long history in the field and has largely progressed by boosting adaptability to complex situation. "Domain Adaptation for Automatic OLED Panel Defect Detection Using Adaptive Support Vector Data Description" (doi:10.1007/s11263-016-0953-y) by Sindagi et al. addresses the real world problem that is a degradation of the classifier performance due to changes in inspection circumstances such as lighting configurations. The authors extend the Support Vector Data Description (SVDD) for adaptively learn an incremental classifier based on a source classifier. They also propose a new feature descriptor using modified Local Binary Pattern (LBP) and local inlier-outlier ratios for detection of OLED panel defects. Experimental results have shown its superiority, especially in micro defects detection. We sleep about one-third of our life, and sleep ergonomics is one of important factors for maintaining our quality of life. "Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis" (doi:10. 1007/s11263-016-0919-0) by Jordi et al. proposes a system for generating a bed mattress prescription by sensing the sleeping human body with a RGB-D sensor (Kinect) and a 2-D pressure sensor. From the measured RGB-D information, the user's body shape parameters (weight, height, BMI, morphotype category) are estimated, and by taking the pressure information and clinical knowledge into account, the most suitable sleep system (mattress-topper-pillow) is recommended by the system. Experiments support usability of the proposed method in real stores. Hyperspectral imaging, due to its rich information, is a popular imaging technique in many machine vision application areas including agriculture and biomedicine. The difficulty in handling hyperspectral images, however, exist in high dimensionality. "Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Restoration" (doi:10. 1007/s11263-016-0921-6) by Fu e al. proposes a novel hyperspectral image (HSI) restoration method that effectively utilizes underlying characteristics of HSIs. The method adaptively learns spatial-spectral dictionary through considering "high correlation across spectra" and "non-local self-similarity over space" in the degraded HIS. Then, an HSI restoration model is designed based on the local and nonlocal sparsity of the HSI under the learned spatial-spectral dictionary. Experimental results show the effectiveness of the proposed method for denoising and superresolution. 123
doi:10.1007/s11263-017-0990-1 fatcat:tytmngwqejdezjsjoj766kqxom