Abstract: An information reception device is provided with: operation surface which is adjusted to produce a characteristic index vibration by an object contact; a storage device stores candidate information (candidate information is related with the index vibration) which serves as a candidate of input information; a microphone which acquires observation information according to observation of the actual vibration arising in the surrounding environment; and a CPU. The CPU (selecting part) judges whether or not the index vibration exists in the observation information acquired. When the CPU judges that the index vibration exists, the CPU selects the candidate information which is related with the index vibration as the input information.

Abstract: Provided is a projection system that sets an arbitrary three-dimensional shape as a projection target and properly corrects geometric distortion of a projected image even when a user's viewpoint is not fixed. A projection unit of the projector apparatus projects a test image for first adjustment. A three-dimensional shape measurement unit measures the three-dimensional shape of the projection target. An image capturing apparatus captures the test image for first adjustment projected by the projection unit to obtain a captured image for first adjustment.

Abstract: An image coding apparatus obtains a quantization parameter of a macroblock to be encoded. The quantization parameter is corrected by adding a correction value thereto. An encoding part encodes the macroblock by using the corrected quantization parameter. After the encoding, a quantization parameter correction part calculates the cumulative target amount of codes by accumulating the target amounts of codes set for the encoded macroblocks, respectively, and calculates the cumulative amount of generated codes by accumulating the respective amounts of generated codes of the encoded macroblocks. If the cumulative amount of generated codes is larger than the cumulative target amount of codes, the quantization parameter correction part increments the correction value. A new macroblock to be encoded is quantized more coarsely than the encoded macroblocks.

Abstract: The object detection device 1 detects an object being recognized (such as a pedestrian) in a frame image 42, and identifies an area 42a where a detected object which is detected in the frame image 42 is present. A frame image 43 is input after the frame image 42. The object detection device 1 detects the object being recognized in the frame image 43, and identifies an area 43a where a detected object which is detected in the frame image 43 is present. When a distance from center coordinates 42b of the area 42a to center coordinates 43b of the area 43a is smaller than a reference distance, the object detection device 1 determines that the detected object which is detected in the frame image 43 is identical to the detected object which is detected in the frame image 42.

Abstract: An object detection device detects an object being recognized (such as a pedestrian) in a frame image, and identifies an area where a detected object which is detected in the frame image is present. A frame image is input after the frame image. The object detection device detects the object being recognized in the frame image, and identifies an area where a detected object which is detected in the frame image is present. When a distance from center coordinates of the area to center coordinates of the area is smaller than a reference distance, the object detection device determines that the detected object which is detected in the frame image is identical to the detected object which is detected in the frame image.

Abstract: In an object detection apparatus 1, a window definer 11 defines a window relative to the location of a pixel in an input image 20. A classification value calculator 13 calculates a classification value indicative of the likelihood that a detection target is present in the window image contained in the window based on the feature data of the detection target. A classification image generator 14 arranges the classification value calculated from the window image according to the pixel location to generate a classification image. An integrator 15 integrates the classification image and a past classification image 42 generated from a past input image input prior to the input image 20 to generate an integrated image 45. A determiner 16 determines whether the detection target is present in the input image 20 based on the integrated image 45.

Abstract: In an object detection apparatus 1, a window definer 11 defines a window relative to the location of a pixel in an input image 20. A classification value calculator 13 calculates a classification value indicative of the likelihood that a detection target is present in the window image contained in the window based on the feature data of the detection target. A classification image generator 14 arranges the classification value calculated from the window image according to the pixel location to generate a classification image. An integrator 15 integrates the classification image and a past classification image 42 generated from a past input image input prior to the input image 20 to generate an integrated image 45. A determiner 16 determines whether the detection target is present in the input image 20 based on the integrated image 45.

Abstract: In an object detection apparatus (1), a window setting unit (12) extracts a window (52) from an edge image (22), to thereby generate an ordinary window image (23). When an edge strength of the ordinary window image (23) is not larger than a usage reference value, a used image determination unit (15) determines to use the ordinary window image (23) for calculation of an identification value (30) indicating a degree of presence of a pedestrian in the window (52). When the edge strength of the ordinary window image (23) is larger than the usage reference value, a pixel value correction unit (13) generates a corrected window image (24) having an edge strength smaller than that of the ordinary window image (23) from the ordinary window image (23). When the edge strength of the corrected window image (24) is not larger than the usage reference value, the used image determination unit (15) determines the corrected window image (24) to be used for calculation of the identification value (30).

Abstract: It's an object of the invention to provide an object detection device capable of detecting an object for detection in an input image with high precision. In an object detection device 1, a detection window setting unit 11 receives a photographic image 21 photographed by a camera. The detection window setting unit 11 sets a detection window area in the photographic image 21 and generates a normal window image 22 by cutting out the detection window area from the photographic image 21. An image processing unit 12 performs image processing such as enlargement and reduction, etc. on the photographic image 21. Each modified window images 23 is cut out from each of the enlarged photographic image 21 and the reduced photographic image 21. A degree calculation unit 13 calculates matching rates indicating a possibility the object for detection in the window image for each window images on the basis of feature data 51 indicating a feature of the object for detection.

Abstract: In a human detection device 1, an edge extractor 11 carries out edge extraction processing to an input image 21 and produces a horizontal edge image 22. A shoulder detector 12 detects a shoulder center and a shoulder width of a person included in the input image 21. A foot detector 13 detects a foot position of the person based on the detected shoulder center and shoulder width. A top detector 14 detects a top position of the person based on the detected shoulder center and shoulder width. A size determiner 15 determines a horizontal size of the person based on the detected shoulder width and determines a vertical size of the person based on the detected foot position and top position. The size determiner 15 produces human range data 28 including the determined sizes, the shoulder center position, the foot position, and the top position.

Abstract: An object detection device detects an object being recognized (such as a pedestrian) in a frame image, and identifies an area where a detected object which is detected in the frame image is present. A frame image is input after the frame image. The object detection device detects the object being recognized in the frame image, and identifies an area where a detected object which is detected in the frame image is present. When a distance from center coordinates of the area to center coordinates of the area smaller than a reference distance, the object detection device determines that the detected object which is detected in the frame image identical to the detected object which is detected in the frame image.

Abstract: In an object detection apparatus (1), a window setting unit (12) extracts a window (52) from an edge image (22), to thereby generate an ordinary window image (23). When an edge strength of the ordinary window image (23) is not larger than a usage reference value, a used image determination unit (15) determines to use the ordinary window image (23) for calculation of an identification value (30) indicating a degree of presence of a pedestrian in the window (52). When the edge strength of the ordinary window image (23) is larger than the usage reference value, a pixel value correction unit (13) generates a corrected window image (24) having an edge strength smaller than that of the ordinary window image (23) from the ordinary window image (23). When the edge strength of the corrected window image (24) is not larger than the usage reference value, the used image determination unit (15) determines the corrected window image (24) to be used for calculation of the identification value (30).

Abstract: An image processing apparatus includes a first storage section and a second storage section, a storage control section, and a computation section. The storage control section sequentially acquires block images obtained as a result of dividing an input image, and stores the block image as a target block image in the first storage section, while storing, in the second storage section, image data of, in a region of the target block image, a region abutting un-inputted block images in the input image, as image data of an abuttal region. The computation section implements a resizing process for changing the size of the target block image by performing an interpolation calculation using image data of the target block image stored in the first storage section and the image data of the abuttal region stored in the second storage section.

Abstract: A statistical value calculation part specifies macroblocks positioned around an object macroblock and calculates a minimum average value of activities of the macroblocks. When images of the macroblocks are flat and the minimum average value is smaller than an activity of the object macroblock, the minimum average value is set as an adjustment value. A correction factor determination part determines a correction factor on the basis of the adjustment value and a factor determination table. By multiplying a reference quantization step value by the correction factor, a quantization step value of the object macroblock is determined. Since the quantization step value reflects a distribution of the activities of the macroblocks, it is possible to suppress a local change of the quantization step value.

Abstract: In an object detection apparatus 1, a window definer 11 defines a window relative to the location of a pixel in an input image 20. A classification value calculator 13 calculates a classification value indicative of the likelihood that a detection target is present in the window image contained in the window based on the feature data of the detection target. A classification image generator 14 arranges the classification value calculated from the window image according to the pixel location to generate a classification image. An integrator 15 integrates the classification image and a past classification image 42 generated from a past input image input prior to the input image 20 to generate an integrated image 45. A determiner 16 determines whether the detection target is present in the input image 20 based on the integrated image 45.

Abstract: In a human detection device 1, an edge extractor 11 carries out edge extraction processing to an input image 21 and produces a horizontal edge image 22. A shoulder detector 12 detects a shoulder center and a shoulder width of a person included in the input image 21. A foot detector 13 detects a foot position of the person based on the detected shoulder center and shoulder width. A top detector 14 detects a top position of the person based on the detected shoulder center and shoulder width. A size determiner 15 determines a horizontal size of the person based on the detected shoulder width and determines a vertical size of the person based on the detected foot position and top position. The size determiner 15 produces human range data 28 including the determined sizes, the shoulder center position, the foot position, and the top position.

Abstract: It's an object of the invention to provide an object detection device capable of detecting an object for detection in an input image with high precision. In an object detection device 1, a detection window setting unit 11 receives a photographic image 21 photographed by a camera. The detection window setting unit 11 sets a detection window area in the photographic image 21 and generates a normal window image 22 by cutting out the detection window area from the photographic image 21. An image processing unit 12 performs image processing such as enlargement and reduction, etc. on the photographic image 21. Each modified window images 23 is cut out from each of the enlarged photographic image 21 and the reduced photographic image 21. A degree calculation unit 13 calculates matching rates indicating a possibility the object for detection in the window image for each window images on the basis of feature data 51 indicating a feature of the object for detection.

Abstract: It is an object to provide a method of calculating a coding cost by which the magnitude relation of the amounts of generated codes can be estimated with high accuracy. A cost calculation part generates a differential block between a coding object block and a prediction block. Hadamard Transform is performed on the differential block to generate a frequency component block. A conversion factor matrix is generated with the information of a quantization matrix reflected thereon. A coding cost is calculated by multiplying components in the frequency component block individually by components in the conversion factor matrix and adding up the multiplied components. A mode selection part selects an optimum predictive coding method on the basis of the coding cost.

Abstract: An image processing apparatus includes a first storage section and a second storage section, a storage control section, and a computation section. The storage control section sequentially acquires block images obtained as a result of dividing an input image, and stores the block image as a target block image in the first storage section, while storing, in the second storage section, image data of, in a region of the target block image, a region abutting un-inputted block images in the input image, as image data of an abuttal region. The computation section implements a resizing process for changing the size of the target block image by performing an interpolation calculation using image data of the target block image stored in the first storage section and the image data of the abuttal region stored in the second storage section.

Abstract: An image coding apparatus obtains a quantization parameter of a macroblock to be encoded. The quantization parameter is corrected by adding a correction value thereto. An encoding part encodes the macroblock by using the corrected quantization parameter. After the encoding, a quantization parameter correction part calculates the cumulative target amount of codes by accumulating the target amounts of codes set for the encoded macroblocks, respectively, and calculates the cumulative amount of generated codes by accumulating the respective amounts of generated codes of the encoded macroblocks. If the cumulative amount of generated codes is larger than the cumulative target amount of codes, the quantization parameter correction part increments the correction value. A new macroblock to be encoded is quantized more coarsely than the encoded macroblocks.