A method is described that includes executing a convolutional neural network layer on an image processor having an array of execution lanes and a two-dimensional shift register. The executing of the convolutional neural network includes loading a plane of image data of a three-dimensional block of image data into the two-dimensional shift register. The executing of the convolutional neural network also includes performing a two-dimensional convolution of the plane of image data with an array of coefficient values by sequentially: concurrently multiplying within the execution lanes respective pixel and coefficient values to produce an array of partial products; concurrently summing within the execution lanes the partial products with respective accumulations of partial products being kept within the two dimensional register for different stencils within the image data; and, effecting alignment of values for the two-dimensional convolution within the execution lanes by shifting content within the two-dimensional shift register array.

An image processing device includes a processor including hardware, the processor being configured to calculate comparison result information through a comparison process performed on the results of a census transform process performed on a first image and the results of the census transform process performed on a second image, set weight information, calculate a cost using the comparison result information and the weight information, calculate the amount of disparity using the cost, set a first weight having as low degree of contribution to the cost on a neighboring pixel which is determined to be affected by noise to a large extent among a plurality of neighboring pixels, and set a second weight having a high degree of contribution to the cost on a neighboring pixel which is determined to be affected by noise to a small extent among the plurality of neighboring pixels.

An image processing method is provided. The method is for calculating a first weighted sum of absolute difference (WSAD) of a first search window and a corresponding first target window, and a second WSAD of a second search window and a corresponding second target window. The first and second search windows have a common matching window, and the first and second target windows have a common target block. The method includes: a) calculating a plurality of absolute differences of the common matching window and the common target block; b) determining a first weight coefficient group and a second weight coefficient group; and c) summing up products of multiplying the absolute differences by the first weight coefficient group to generate the first WSAD, and summing up products of multiplying the absolute differences by the second weight coefficient group to generate the second WSAD.

An image processing apparatus counts at least one of the number of pixels having an identical color to a target pixel, the number of pixels having a similar color to the target pixel, and the number of pixels having a different color from the target pixel in a target window, and determines an attribute of the target pixel based on a result of the counting.

A system for estimating a Lambertian equivalent reflectance for reflective band imagery is disclosed. In some embodiments, the system estimates an equivalent reflectance and performs atmospheric correction of reflective band imagery without user interaction and accounts for the effect of background reflectance mixing with individual target reflectances. Some of these embodiments use a dark pixel-based technique to improve the characterization of the atmosphere.

Described herein is a method for detecting keypoints in three-dimensional images in which a three-dimensional image of a scene captured by a depth sensing imaging system is processed using a distance-independent keypoint filter. Keypoints are derived from the three-dimensional image by determining a mean shift field and using x- and y-components of the mean shift field to derive intersections of 0-isolines thereof. Positive and negative keypoints or nodes are connected to one another, positive to positive and negative to negative, to form a keygraph structure.

Methods, apparatus, and articles of manufacture providing an efficient safety mechanism for signal processing hardware are disclosed. An example apparatus includes an input interface to receive an input signal; a hardware accelerator to process the input signal, the hardware accelerator including: unprotected memory to store non-critical data corresponding to the input signal; and protected memory to store critical data corresponding to the input signal; and an output interface to transmit the processed input signal.

The present invention relates to a method of identifying a brand and a model of a vehicle. The method comprises obtaining one or more video frames. Further, the method comprises detecting one of presence or absence of text in the at least one vehicle. Upon detecting the presence of the text, the method comprises determining at least one of the brand and the model of the at least one vehicle by accumulating the text recognized in the one or more video frames. Further, identifying at least one of the brand and the model of the at least one vehicle based on an accumulated text. Upon detecting the absence of the text, the method comprises determining the brand of the at least one vehicle based on logo associated with the vehicle.

A device is able to be used to detect an illness and/or symptoms of the illness in a user by utilizing a body fluid detector and/or other devices. The device is also able to determine when additional devices of users come within a specified distance of the device. An alert regarding a diagnosis and/or analysis of the symptoms of the illness is able to sent to a central server, a cloud device or another device to share the diagnosis and/or the analysis of the symptoms of the illness with the additional devices of users.

According to one embodiment, an image processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data. The processing circuitry is configured to obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns based on the medical image data. The processing circuitry is configured to calculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns.