Search asymmetries for threatening faces in chimpanzees (Pan troglodytes)

For primates, the ability to efficiently detect threatening faces is highly adaptive, however, it is not clear exactly how faces are detected. This study investigated whether chimpanzees show search asymmetries for conspecific threatening faces featuring scream and bared teeth expressions. Five adult female chimpanzees participated in a series of touchscreen matching-to-sample visual search tasks. In Experiment 1, search advantages for scream versus neutral targets, and scream versus bared teeth targets were found. A serial search strategy indicated greater difficulty in disengaging attention from scream versus neutral distractors. In Experiments 2a and 2b, search advantages for scream versus neutral targets remained when the mouth was darkened, suggesting that the brightness contrast of the mouth was not critical for the efficient detection of scream targets. In Experiments 3a and 3b, search advantages for inverted scream versus neutral targets disappeared, indicating configural processing. Together, exclusion of the brightness contrast of the mouth as a low-level perceptual confound, and evidence of configural processing, suggested the scream faces may have been perceived as threatening. However, the search advantage for scream faces is most likely explained by the presence of teeth, independently of threat. The study provides further support that an attentional bias towards threatening faces is a homologous trait, which can be traced back to at least the last common ancestor of Old-World monkeys and apes.

Implementing the basic CT backprojecting algorithm in android mobile application

We have implemented the basic steps for the FDK backprojecting algorithm in computed tomography. Application works from the set of preloaded projections and uses OpenCV libraries for FFT, convolution, frequency space image filtering, image’s brightness, contrast and quality manipulation. Compared to the desktop implementation, the calculation-intensive part of the application was moved to the asynchronous background task hosted by an android fragment. This allows the task to survive the application’s configuration changes and to run in the background even if the main activity was destroyed. The minimalistic interface with the access to all main backprojecting parameters was implemented. The result of backprojection is saved as an image in the download folder of the phone. The user also has the control over the reconstructed slice location along the Z axis.

Inseparable color and form in mouse visual cortex described by random pooling from rods and cones

An image projected onto the retina is composed of local contrasts in color and brightness, both of which can aid in any visual perception task. Recent investigations of the mouse ventral retina demonstrate that rod and cone responses are combined to detect changes between UV and green light, thus providing a new model for color vision. An important question is how the spatial representations of both color and brightness contrast are transformed by downstream circuits. Its known that SF tuning of brightness contrast is sharpened at the level of mouse primary visual cortex, yet color contrast is untested. Here, we presented sinewave gratings that drive one of four axes of rod and cone contrast space, including brightness contrast (rod+cone) and color contrast (rod-cone). We find that V1 neurons are tuned to higher spatial frequencies of brightness contrast than color contrast, and are most responsive to color at the lowest spatial frequencies. These results are consistent with a model of single-opponency between rods and cones, but do not match its classic description. The data can instead be described by a simple model of convergent ON and OFF inputs to V1, which randomly pool discrete quantities of each photoreceptor class. Unlike classic depictions of single-opponency, this model requires minimal constraints on the circuit, accounts for our observed bandpass spatial frequency tuning of rod and cone isolating contrast, and is consistent with recent studies showing unselective pooling from photoreceptors in the retina.

IMAGE QUALITY IMPROVEMENT USING IMAGE PROCESSING METHOD IMAGE BRIGHTNESS CONTRAST AND IMAGE SHARPENING

The ability of computers that are increasingly reliable in various fields, especially in helping the image processing sector through improving image quality, is very much felt so that the empowerment of computers at any time needs to be improved. Image quality improvement can be made with various techniques, including Image Quality Improvement with Image Brightness and Image Sharpening methods. The process begins with capturing the image and then continues with increasing the intensity of brightness, image contrast and sharpening. Image processing results are indicated by changes in the resulting image and changes in the image histogram

BRIGHTNESS-CONTRAST MAGNIFIER ALGORITHM AND METHODS OF ITS ENHANCING

The paper considers the algorithmic basis of the developed application, which allows the user to select image fragments for viewing not only in enlarged form, but also with increased detail distinctness through brightness-contrast transformations. There has been proposed an algorithm of upsizing and processing the selected image fragment according to the required parameters of average brightness and contrast. The advantages of the proposed method of image processing in comparison with global methods for processing the entire image are investigated. The considered approach develops the advantages when marking low-contrast fragments that are close to monotone in images with fragments of different brightness. The experiments on processing various fragments of images were carried out using a specially developed program. The examples of results have been presented. The behavior of two types of fragments on simplified models of pixel brightness distribution has been analyzed, and for this reason there was made a conclusion about further improving the approach.

Liver and Tumor Segmentation Techniques for CT Abdominal Images

Image segmentation is one of the important step in digital image processing where the images are partitioned into different segments based on several properties like brightness, contrast, intensity and texture. Image processing includes several steps among which image segmentation is the difficult task. Accurate segmentation is the fundamental step in digital image processing. Segmentation can be performed manually, but as it is a tedious task, automatic segmentation techniques which gives more accuracy has to be found. Many recent segmentation techniques for liver image segmentation are discussed here. Some of the techniques to segment liver from CT scanned abdominal image and to segment tumor from the liver are discussed. The main objective is to highlight various techniques which can aid in developing a novel segmentation technique.