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Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants’ memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.

Analysis of eye movement due to different visual stimuli always has been one of the major research areas in vision science. An important category of works belongs to decoding of eye movement due to variations of color of visual stimuli. In this research, for the first time, we employ fractal analysis in order to investigate the variations of complex structure of eye movement time series in response to variations of color of visual stimuli. For this purpose, we applied two different images in three different colors (red, green, blue) to subjects. The result of our analysis showed that eye movement has the greatest complexity in case of green visual stimulus. On the other hand, the lowest complexity of eye movement was observed in case of red stimulus. In addition, the results showed that except for red visual stimulus, applying the visual stimulus with greater complexity causes the lower complexity in eye movements. The employed methodology in this research can be further applied to analyze the influence of other variations of visual stimuli on human eye movement.

The perception of an image in one's mind filled with vibrant colors comes so naturally for humans that the complexity of the human visual system is often overlooked. This paper follows the journey of light through the human eye and its interpretation in the brain. The wave properties of light are explored as light propagates through the eye while the particle properties of light are examined when light is absorbed by the retina. The computations involved in the perception of color are discussed as well as birefringent properties of the eye.

The research work has focused on detection and prediction of melanoma which is done by subjecting to features extraction, where the features of an image consisting of melanoma regions are detected by analysis and this analysis is done by considering the features like color and texture-based features learning strategy. These features are extracted by combining color and texture-based features extraction with deep convolutional features representation learning strategy. The colors of images are extracted by representing the colors of different channels into red, green and blue channel information. The combination of texture features extraction with color-based features extraction in addition to Alex net features extraction learning has made the system more robust and efficient toward the segmentation and classification of images. Further, the erected method involves convoluting the features of extracted information with color and texture-based method which has led our system to full convolution neural networks with images features extraction. The melanoma is detected and segmented with watershed segmentation, these segmented features are subjected to the proposed features extraction method, where the features are extracted by combining the methods of texture with color-based information. These colors are made available to the proposed method by analyzing the regions of melanoma images. The erected method does the task of features extraction by Weber law descriptors in combination with red, green, blue channels information extracted from features representation learning. The proposed method has yielded an accuracy of 94.12% of segmentation accuracy and a classification accuracy of 94.32% with respect to various other classification techniques.

The eminent Chinese artist LaoZhu has created a homogeneous set of abstract pictures that are referred to as the “third abstraction.” By definition, these pictures are meant to be representations of the artist’s personal involvement and as such to create an internal point of view in the observer on an implicit level of processing. Aiming at investigating whether the artist’s choice of a specific color is experienced in a specific way by the recipient, we assessed both explicit and implicit (i.e., neuro-cognitive) correlates in naive viewers of LaoZhu’s pieces. The behavioral results reveal a preference of the original red paintings over color-changed counterparts in green or black. Paradoxically and inconsistent with predictions, we found higher levels of neural activation in several brain regions (predominantly in the frontal and parietal cortices) for the color-changed compared to the original red conditions. These observations add empirically to the complementarity of early visual pathways and higher-order cognition as well as of explicit and implicit information processing during aesthetic appreciation. We discuss our findings in light of processing effort and top-down control of the color-changed paintings. With regard to the third abstraction as defined by LaoZhu, in particular to the distinction between an external and internal point of view when viewing abstract art, our results contribute to an understanding of “abstraction and empathy” as a fundamental part of aesthetic appreciation.

Colors are critical for understanding the emotional aspect of the human artistic mind, such as that found in painting a landscape, still life, or portrait. First, we report how single colors are memorized in the brain; second, how pairs of colors harmonize in the dissociated brain under the influence of the emotional brain; third, we see how colored paintings are appreciated as beautiful or ugly in the dissociated brain areas led by the intrinsic reward system in the human brain. The orbitofrontal brain is probably one of the vital brain areas that brings us a value-based reward system that makes a unique contribution to emotional neuroaesthetics.