Abstract: A tone adjustment unit assumes that the following relationship is satisfied: LO=DO+Da=TD(I)+Da=TL(I), in which I represents a luminance value normalized to have a maximum value of one in first image data, DO represents a luminance value normalized to have a maximum value of one in second image data, Da represents a luminance value under ambient light during viewing, LO represents a luminance value of the second image data while taking into consideration effects of the ambient light during viewing, TD(I) represents a tone curve used for conversion, and TL(I) represents a tone curve taking into consideration the environment. The tone curve TD(I) is adjusted so that a function representing an inclination ?(I) after converting LO=TL(I) to a double logarithmic plot is non-zero over an entire dynamic range.

Abstract: A tone adjustment unit assumes that the following relationship is satisfied: Lo=Do+Da=TD(I)+Da=TL(I), in which I represents a luminance value normalized to have a maximum value of one in first image data, Do represents a luminance value normalized to have a maximum value of one in second image data, Da represents a luminance value under ambient light during viewing, Lo represents a luminance value of the second image data while taking into consideration effects of the ambient light during viewing, TD(I) represents a tone curve used for conversion, and TL(I) represents a tone curve taking into consideration the environment. The tone curve TD(I) is adjusted so that a function representing an inclination ?(I) after converting Lo=TL(I) to a double logarithmic plot is non-zero over an entire dynamic range.

Abstract: Techniques are provided in which a “dichromat” (e.g., a color-blind person) is able to identify the hue of an object without determining the color difference signals that are applied to the original color image. Such features may be implemented by performing hue rotation processing on each pixel data of the original color image captured.

Abstract: An image processing apparatus (100) is formed including: color image acquisition means (222) for acquiring a color image of an object, which is expressed by a luminance signal, a first color difference signal that is a color difference signal of a color pair that is easy to be identified by a dichromat and a second difference signal that is a color difference signal of a color pair that is difficult to be identified by the dichromat; color difference signal processing means (223) for performing mixing reprocessing that mixes a component of the second color difference signal with a component of the first color difference signal, for each pixel data of the color image; and display control means (230) for performing control to display the color image processed by the color difference signal processing means (223) on a display unit (109) as a color image subjected to color conversion. By this means, it is possible to provide a mechanism in which a dichromat can understand the hue of an object without learning.

Abstract: There are provided: an original color image capturing unit capturing an original color image of a object represented by YCbCr; a Cr component elimination processing unit performing, on each pixel data of the original color image captured by the original color image capturing unit, processing to make a value of Cr component to be zero; a hue rotation processing unit performing, on each pixel data of the color image after being subjected to the processing performed by the Cr component elimination processing unit, hue rotation processing of 5 degrees to 50 degrees in a CbCr color space; and a display control unit performing a control to display the color image after being subjected to the hue rotation processing performed by the hue rotation processing unit, on a display section as a hue-converted color image.

Abstract: A hue conversion unit (124) converts a value of a first color difference (i.e., chrominance) signal (color difference signal of color pair hard to be distinguished by dichromat) obtained by a luminance signal/color difference signal conversion unit (123) into a value of a second color difference signal (color difference signal of color pair easy to be distinguished by dichromat), and converts a value of the second color difference signal obtained by the luminance signal/color difference signal conversion unit (123) into a value of the first color difference signal. A hue-converted color image generation unit (127) generates a hue-converted color image based on a luminance signal and the first color difference signal after conversion and the second color difference signal after conversion which are obtained by the hue conversion unit (124).

Abstract: The method for measuring speed is performed by dividing the time sequence image data A into long and narrow frames (step 401). Next, the framed images and the time sequence image data B produced by the line scan camera B are correlated to obtain a moving time (step 402). Next, a moving time of each framed image and a corresponding moving distance are obtained to compute a moving speed (step 403). Next, scale of a specific shape pattern and the like is corrected using the moving speed obtained to match the scales in the two image sequences A and B to obtain a similarity degree (step 404). Next, a threshold value is applied to the similarity degree to recognize the specific shape pattern, thereby recognizing that the images are produced by a moving vehicle.

Abstract: A tomographic image reading method for extracting a comparison image corresponding to a diagnostic image, the diagnostic image being one of first tomographic images, the comparison image being one of second tomographic images, the method including the steps of: inputting the first images and the second images; generating a first projection image from the first images and a second projection image from the second images; measuring shift amount between the first projection image and the second projection image by using a template; correcting the slice position according to the shift amount; and displaying the diagnostic image and the comparison image to a monitor.

Abstract: The method for measuring speed is performed by dividing the time sequence image data A into long and narrow frames (step 401). Next, the framed images and the time sequence image data B produced by the line scan camera B are correlated to obtain a moving time (step 402). Next, a moving time of each framed image and a corresponding moving distance are obtained to compute a moving speed (step 403). Next, scale of a specific shape pattern and the like is corrected using the moving speed obtained to match the scales in the two image sequences A and B to obtain a similarity degree (step 404). Next, a threshold value is applied to the similarity degree to recognize the specific shape pattern, thereby recognizing that the images are produced by a moving vehicle.

Abstract: The method for measuring speed is performed by dividing the time sequence image data A into long and narrow frames (step 401). Next, the framed images and the time sequence image data B produced by the line scan camera B are correlated to obtain a moving time (step 402). Next, a moving time of each framed image and a corresponding moving distance are obtained to compute a moving speed (step 403). Next, scale of a specific shape pattern and the like is corrected using the moving speed obtained to match the scales in the two image sequences A and B to obtain a similarity degree (step 404). Next, a threshold value is applied to the similarity degree to recognize the specific shape pattern, thereby recognizing that the images are produced by a moving vehicle.

Abstract: A method for extracting a pre-selected pattern from an image by a programmed computer is able to identify and extract the pattern even under adverse conditions such as presence of noise or shadow in the image or the target pattern is partially shielded. The steps are: storing reference patterns for referencing; storing input images; applying filtering processes to reference patterns and input images; integrating filtered results of the reference pattern and filtered results of the input image to generate integrated results; and extracting a reference pattern according to the integrated results and specifying a location of the reference pattern in the input image. An image abnormality detection can also be performed using the basic method to identify a normal scene and detect a deviation from normalcy even under adverse conditions.

Abstract: When right and left images are presented on a projection plane which is a display plane with binocular disparity, an object located in front of projection plane P.sub.s can be perceived at the right position by accurately calculating the corresponding points while avoiding vignetting due to the picture frame of projection plane P.sub.s or by making it appear as if an occluding object at the frontmost plane (i.e. virtual frame VF) were present.