Abstract: An electronic apparatus and a compression method for an artificial neural network are provided. The compression method is adapted for the artificial neural network with a plurality of convolution layers. The compression method includes: setting a first pruning layer for coupling the first pruning layer to Lth convolution layer, where the first pruning layer has a plurality of first weighting values and each of the first weighting values corresponds to each of a plurality of channels of the Lth convolution layer; tuning the first weighting values, selecting a part of the channels of the Lth convolution layer to be at least one first redundancy channel according to the first weighting values, and generating a compressed Lth convolution layer by deleting the at least one first redundancy channel; and removing the first pruning layer, and generating a first compressed artificial neural network.

Abstract: An electronic apparatus and a compression method for an artificial neural network are provided. The compression method is adapted for the artificial neural network with a plurality of convolution layers. The compression method includes: setting a first pruning layer for coupling the first pruning layer to Lth convolution layer, where the first pruning layer has a plurality of first weighting values and each of the first weighting values corresponds to each of a plurality of channels of the Lth convolution layer; tuning the first weighting values, selecting a part of the channels of the Lth convolution layer to be at least one first redundancy channel according to the first weighting values, and generating a compressed Lth convolution layer by deleting the at least one first redundancy channel; and removing the first pruning layer, and generating a first compressed artificial neural network.

Abstract: An image feature extraction method for a 360° image includes the following steps: projecting the 360° image onto a cube model to generate an image stack including a plurality of images having a link relationship; using the image stack as an input of a neural network, wherein when operation layers of the neural network performs padding operation on one of the plurality of images, the link relationship between the plurality of adjacent images is used such that the padded portion at the image boundary is filled with the data of neighboring images in order to retain the characteristics of the boundary portion of the image; and by the arithmetic operation of the neural network of such layers with the padded feature map, an image feature map is generated.

Abstract: The present invention discloses an object-detection method and a multi-class Bhattacharyya Boost algorithm used therein, wherein firstly, integral images are calculated from an image data in order to speed up the extraction of the characteristics of the objects; then, multiple rectangles of different sizes are scanned at different locations of the image data, and the multi-class Bhattacharyya Boost algorithm is used to detect multi-class objects. In the present invention, the detection framework can use only one single boosted cascade to determine the status and position of the object inside the image data. The simultaneous multi-class detection of the present invention can effectively overcome the detection difficulties resulting from the diversification of object appearances under different conditions.

Abstract: The present invention discloses an object-detection method and a multi-class Bhattacharyya Boost algorithm used therein, wherein firstly, integral images are calculated from an image data in order to speed up the extraction of the characteristics of the objects; then, multiple rectangles of different sizes are scanned at different locations of the image data, and the multi-class Bhattacharyya Boost algorithm is used to detect multi-class objects. In the present invention, the detection framework can use only one single boosted cascade to determine the status and position of the object inside the image data. The simultaneous multi-class detection of the present invention can effectively overcome the detection difficulties resulting from the diversification of object appearances under different conditions.