Aesthetic Image Statistics Vary with Artistic Genre

Research to date has not found strong evidence for a universal link between any single low-level image statistic, such as fractal dimension or Fourier spectral slope, and aesthetic ratings of images in general. This study assessed whether different image statistics are important for artistic images containing different subjects and used partial least squares regression (PLSR) to identify the statistics that correlated most reliably with ratings. Fourier spectral slope, fractal dimension and Shannon entropy were estimated separately for paintings containing landscapes, people, still life, portraits, nudes, animals, buildings and abstracts. Separate analyses were performed on the luminance and colour information in the images. PLSR fits showed shared variance of up to 75% between image statistics and aesthetic ratings. The most important statistics and image planes varied across genres. Variation in statistics may reflect characteristic properties of the different neural sub-systems that process different types of image.

Development of Edge-Based Lane Detection Algorithm using Image Processing

Image processing is known as the process of converting an image into a digital form so as to obtain an enhanced image and to extract useful information from it. This paper presents a simple step by step analysis of edges-based lane detection. Some of the known and common edge detection techniques such as Sobel, Canny, Prewitt and Roberts were studied and evaluated using image segmentation, morphology, image statistic and Hough Transform. The result indicated some similarities in the process as well as major differences. These differences were observed to be as a result of the high sensitivity of the edge detector in detecting noise such as cast shadows and unmarked lanes. This could be noticed in the case of canny edge detector. Also these data could be considered in the development of a multi-system edge detector, which could be used to analyze various road scenes and runs the appropriate edge detector best suited for the current situation.

A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast

Humans are very adept at extracting the “gist” of a scene in a fraction of a second. We have found that radiologists can discriminate normal from abnormal mammograms at above-chance levels after a half-second viewing (d′ ∼ 1) but are at chance in localizing the abnormality. This pattern of results suggests that they are detecting a global signal of abnormality. What are the stimulus properties that might support this ability? We investigated the nature of the gist signal in four experiments by asking radiologists to make detection and localization responses about briefly presented mammograms in which the spatial frequency, symmetry, and/or size of the images was manipulated. We show that the signal is stronger in the higher spatial frequencies. Performance does not depend on detection of breaks in the normal symmetry of left and right breasts. Moreover, above-chance classification is possible using images from the normal breast of a patient with overt signs of cancer only in the other breast. Some signal is present in the portions of the parenchyma (breast tissue) that do not contain a lesion or that are in the contralateral breast. This signal does not appear to be a simple assessment of breast density but rather the detection of the abnormal gist may be based on a widely distributed image statistic, learned by experts. The finding that a global signal, related to disease, can be detected in parenchyma that does not contain a lesion has implications for improving breast cancer detection.

An Efficient Detail-Preserving Random-Valued Impulse Noise Filter

This paper presents an efficient detail-preserving random-valued impulse noise filter. The proposed method introduced an image statistic, the decision-based rank ordered absolute differences (DROAD for short), to distinguish image details from impulses. This reduces the probability of detecting image details as impulses. Besides, in order to search for suitable thresholds at noise detecting phase, we present the Q-estimate of variance in noisy image. According to image variance, we define a threshold for each pixel. This makes more impulses can be identified. Experiments results show that our filter provides a significant improvement over many other existing techniques.

An Efficient Pre-Processing Algorithm for Removing Uniform Noise Based on Cluster Method

An efficient pre-processing algorithm for removing uniform noise is proposed. Local image statistic information and human visual perception are used to classify the pixels in the filter window. According to the elements number of each cluster, all pixels are divided to noise-free clusters or fuzzy clusters. Through cluster method, almost all noise pixels are identified and then restored. Finally, we choose some commonly used filters to test our algorithm. The experimental results tell that our approach can enhance those filters’ capability of suppressing impulse noise effectively. Due to the proposed algorithm can decrease the noise density effectually and keep image details, it can be introduced into many existing uniform noise filtering techniques.