Special issue Visual system and image technology. 4. Neural mechanisms of information processing and recognizing. 3. Computer vision.

AbstractObjective image quality assessment (IQA) plays an important role in various visual communication systems, which can automatically and efficiently predict the perceived quality of images. The human eye is the ultimate evaluator for visual experience, thus the modeling of human visual system (HVS) is a core issue for objective IQA and visual experience optimization. The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively, while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity. For bridging the gap between signal distortion and visual experience, in this paper, we propose a novel perceptual no-reference (NR) IQA algorithm based on structural computational modeling of HVS. According to the mechanism of the human brain, we divide the visual signal processing into a low-level visual layer, a middle-level visual layer and a high-level visual layer, which conduct pixel information processing, primitive information processing and global image information processing, respectively. The natural scene statistics (NSS) based features, deep features and free-energy based features are extracted from these three layers. The support vector regression (SVR) is employed to aggregate features to the final quality prediction. Extensive experimental comparisons on three widely used benchmark IQA databases (LIVE, CSIQ and TID2013) demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

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IJCARS—IPCAI 2021 Special Issue: Information Processing for Computer-Assisted Interventions, 12th International Conference 2021—Part 1

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02401-5 ◽

2021 ◽

Author(s):

Alfred Franz ◽

Orcun Goksel

Keyword(s):

Information Processing ◽

Computer Assisted ◽

Special Issue ◽

International Conference

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Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

Evolutionary Computation ◽

10.1162/evco_a_00146 ◽

2016 ◽

Vol 24 (1) ◽

pp. 143-182 ◽

Cited By ~ 10

Author(s):

Harith Al-Sahaf ◽

Mengjie Zhang ◽

Mark Johnston

Keyword(s):

Computer Vision ◽

Pattern Recognition ◽

Genetic Programming ◽

Visual System ◽

Image Classification ◽

Human Visual System ◽

Binary Classification ◽

Programming Approach ◽

Data Sets ◽

New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

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