Abstract: An apparatus capable of detecting location of object contained in image data and its detecting method are disclosed. The apparatus comprises an image capturing module, a weight assignment module, and a processing module. The image capturing module is for capturing an image. The weight assignment module performs the pixel weight/probability assignment according to the priori information and the image, and figures out the initial gravity center of the object according to the object location initialization. The processing module performs the statistical analysis according to the result of the pixel weight/probability assignment and the initial gravity center of the object so as to obtain the analysis result and update the object location. The processing module determines whether or not the analysis result meets the preset value, if it does, the processing module outputs an estimated result; if it doesn't, the processing module repeats the foregoing processes.

Abstract: An object tracking method includes steps of obtaining multiple first classifications of pixels within a first focus frame in a first frame picture, wherein the first focus frame includes an object to be tracked and has a first rectangular frame in a second frame picture; performing a positioning process to obtain a second rectangular frame; and obtaining color features of pixels around the second rectangular frame sequentially and establishing multiple second classifications according to the color feature. The established second classifications are compared with the first classifications sequentially to obtain an approximation value, compared with a predetermined threshold. The second rectangular frame is progressively adjusted, so as to establish a second focus frame. By analyzing color features of the pixels of the object and with a classification manner, the efficacy of detecting a shape and size of the object so as to update information of the focus frame is achieved.

Abstract: A vehicle apparatus control system and method thereof are disclosed. The vehicle apparatus control system comprises a data storage module, an image capturing module, a face recognition module and a control module. The data storage module stores multiple registered users' facial feature parameters and vehicle apparatus setting parameters, and integration setting data. The face recognition module detects several facial images contained in the captured image and recognizes the users corresponding to the face images according to the facial feature parameters stored in data storage module. The control module set a vehicle apparatus according to the integration setting data, and the corresponding users' vehicle apparatus setting parameters.

Abstract: An apparatus capable of detecting location of object contained in image data and its detecting method are disclosed. The apparatus comprises an image capturing module, a weight assignment module, and a processing module. The image capturing module is for capturing an image. The weight assignment module performs the pixel weight/probability assignment according to the priori information and the image, and figures out the initial gravity center of the object according to the object location initialization. The processing module performs the statistical analysis according to the result of the pixel weight/probability assignment and the initial gravity center of the object so as to obtain the analysis result and update the object location. The processing module determines whether or not the analysis result meets the preset value, if it does, the processing module outputs an estimated result; if it doesn't, the processing module repeats the foregoing processes.

Abstract: An automobile equipment control system and a control method thereof. The system comprises an input module, a storage module, an image capturing module, a first recognizing module, a second recognizing module and a processing module. The input module is used to input a plurality of vehicle equipment setting values. The storage module is used to save a plurality of facial characteristic values and a plurality of setting values of vehicle equipment. The image capturing module is used to capture the image of the user. The first recognizing module analyzes the facial image in the image and extracts a facial characteristic point from the facial image. The second recognizing module analyzes the user's hand gesture, the user's head gestures or what the user speaks so as to decide which setting value of vehicle equipment is adopted to control the vehicle equipment.

Abstract: A multidirectional face detection method is for detecting a face in a picture under detection at different positions. The face detection method includes the steps. A selecting window sets to sequentially select different sub-image patterns from the picture under detection. A facial feature weight calculates and it is calculated according to a feature value of the pixels in a sub-image pattern selected by the selecting window, thereby determining if the sub-image pattern has any features similar to the face. A facial edge weight calculates for made on the picture under detection according to a boundary value of the pixels in the sub-image pattern selected by the selecting window, so as to determine if the part of area of the picture under detection has any facial-boundaries. Profile detection is performed to respectively mark the facial-boundaries in the sub-image patterns with arc segments respectively for the sub-image patterns having the facial-boundaries.

Abstract: A hierarchical face recognition training method and a hierarchical face recognition method thereof for performing a face feature recognition on an image under detection. The method includes a training process and a recognition process. The recognition method includes the steps. A plurality of training samples is obtained. The training samples are subdivided into a plurality of sub-image categories according to a plurality of angle intervals, and the training of a plurality of face features performs on a corresponding sub-image detector of each of the sub-image categories. The training measures performed repeatedly to generate sub-image categories at a sub-level of the sub-image categories. The training method includes the steps. An image under detection is loaded. A similarity of each of sub-image detectors compares according to the image under detection, and the sub-image detector having the highest similarity is selected. The face recognition measures performed repeatedly on the selected sub-image detector.

Abstract: A vehicle apparatus control system and method thereof are disclosed. The vehicle apparatus control system comprises a data storage module, an image capturing module, a face recognition module and a control module. The data storage module stores multiple registered users' facial feature parameters and vehicle apparatus setting parameters, and integration setting data. The face recognition module detects several facial images contained in the captured image and recognizes the users corresponding to the face images according to the facial feature parameters stored in data storage module. The control module set a vehicle apparatus according to the integration setting data, and the corresponding users' vehicle apparatus setting parameters.

Abstract: A method of correcting a false-color pixel in a digital image is used to correct a false-color pixel generated after being processed by a color correction matrix (CCM). The method includes the following steps. Obtaining a raw image, for being processed by the CCM operation to generate a processed image; comparing pixels of the raw image with that of the processed image according to a color difference ratio, to find out a false-color pixel in the pixels of the processed image; then, performing a color correction on the false-color pixel at the corresponding position of the raw image and the processed image. The false-color pixel is corrected according to the pixel color at the corresponding position of the raw image.

Abstract: An object tracking method includes steps of obtaining multiple first classifications of pixels within a first focus frame in a first frame picture, wherein the first focus frame includes an object to be tracked and has a first rectangular frame in a second frame picture; performing a positioning process to obtain a second rectangular frame; and obtaining color features of pixels around the second rectangular frame sequentially and establishing multiple second classifications according to the color feature. The established second classifications are compared with the first classifications sequentially to obtain an approximation value, compared with a predetermined threshold. The second rectangular frame is progressively adjusted, so as to establish a second focus frame. By analyzing color features of the pixels of the object and with a classification manner, the efficacy of detecting a shape and size of the object so as to update information of the focus frame is achieved.

Abstract: A method of adjusting selected window size of an image object is applicable for tracking a target object in a video. The video includes a plurality of frames, and the target object has a display range changing with the playback of each frame. According to a variation trend of the display range of the target object, whether a variation times corresponding to the variation trend reaches a threshold value or not is recorded, and then the selected window size is reset, such that the target object is enclosed with a selected window having a size closer to the target object.

Abstract: A method of reducing image noise is provided. In the method, during the process of reducing image noise by a bilateral filter, the image noise filtering intensity of a domain filter in the bilateral filter is adjusted according to whether a regional area of the image has a frame boundary, and the image noise filtering intensity of a range filter in the bilateral filter is adjusted through the intensity of a regional area, so as to improve the image noise reduction effect of the bilateral filter.

Abstract: A hierarchical face recognition training method and a hierarchical face recognition method thereof for performing a face feature recognition on an image under detection. The method includes a training process and a recognition process. The recognition method includes the steps. A plurality of training samples is obtained. The training samples are subdivided into a plurality of sub-image categories according to a plurality of angle intervals, and the training of a plurality of face features performs on a corresponding sub-image detector of each of the sub-image categories. The training measures performed repeatedly to generate sub-image categories at a sub-level of the sub-image categories. The training method includes the steps. An image under detection is loaded. A similarity of each of sub-image detectors compares according to the image under detection, and the sub-image detector having the highest similarity is selected. The face recognition measures performed repeatedly on the selected sub-image detector.

Abstract: A method of correcting a false-color pixel in a digital image is used to correct a false-color pixel generated after being processed by a color correction matrix (CCM). The method includes the following steps. Obtaining a raw image, for being processed by the CCM operation to generate a processed image; comparing pixels of the raw image with that of the processed image according to a color difference ratio, to find out a false-color pixel in the pixels of the processed image; then, performing a color correction on the false-color pixel at the corresponding position of the raw image and the processed image. The false-color pixel is corrected according to the pixel color at the corresponding position of the raw image.

Abstract: Face detection and tracking method is executed by a computer or a microprocessor with computing capability for identifying human faces and positions thereof in image frames. First, face detection is performed to detect human faces in a plurality of frames. Then, face tracking is performed on each of the frames to track the detected human faces and record positions of these human faces. Afterward, face detection on the image frames is again performed every few frames, skipping the positions of the human faces that have been recorded, so as to quickly search for other human faces that might be newly added.

Abstract: A method of adjusting selected window size of an image object is applicable for tracking a target object in a video. The video includes a plurality of frames, and the target object has a display range changing with the playback of each frame. According to a variation trend of the display range of the target object, whether a variation times corresponding to the variation trend reaches a threshold value or not is recorded, and then the selected window size is reset, such that the target object is enclosed with a selected window having a size closer to the target object.

Abstract: A multidirectional face detection method is used to detect a face in a picture under detection at different positions. The face detection method includes the steps. A selecting window sets to sequentially select different sub-image patterns from the picture under detection. A facial weight calculates and it is calculated according to a feature value of the pixels in a sub-image pattern selected by the selecting window, thereby determining if the sub-image pattern has any features similar to the face. A facial edge weight calculates for made on the picture under detection according to a boundary value of the pixels in the sub-image pattern selected by the selecting window, so as to determine if the part of area of the picture under detection has any facial-boundaries. A profile detection performs to respectively mark the facial-boundaries in the sub-image patterns with a arc segments respectively for the sub-image patterns having the facial-boundaries.

Abstract: An image processing method for adjusting a scale of a face is implemented by a microprocessor of a digital camera. The microprocessor performs a face detection to find the position of a face in the image, and sets a zoom block according to an entire column/row of the position of the face, so as to zoom in/out the scale of the face. When the scale of the face is adjusted, an entire column of pixels are added/deleted with constrain proportions from two sides of the zoom block, for repairing the pixels altered during the zoom in/out of the face so as to remain the whole image undistorted. This method finds the position of the face accurately, and adjusts the pixels at the position of the face, so as to effectively adjust the scale of the face, and avoid disproportion between the face and the body in the image.

Abstract: A graduated processing method of digital image is adapted to a digital camera for immediately adjusting brightness in different regions of a shot digital image. The processing method includes the following steps. An image capturing device determines an image boundary of a digital image according to an image difference condition in the digital image. Then, a graduated layer is used in the digital image along a vertical direction of the image boundary, and a brightness difference in different regions of the digital image is adjusted.

Abstract: A method of reducing image noise is provided. In the method, during the process of reducing image noise by a bilateral filter, the image noise filtering intensity of a domain filter in the bilateral filter is adjusted according to whether a regional area of the image has a frame boundary, and the image noise filtering intensity of a range filter in the bilateral filter is adjusted through the intensity of a regional area, so as to improve the image noise reduction effect of the bilateral filter.