Abstract: A scene analysis method is applied to a visual navigation device. The scene analysis method includes the steps of capturing scene information at different times based upon the field of view of an image capturing device; analyzing different targets existing in the captured scene information; comparing each one of the targets captured at different times to classify the target, wherein the target that is in a specific region in the field of view and moving toward the image capturing device is classified as a type A target; otherwise the target is classified as a type B target. When the visual navigation is installed on a vehicle, a protection procedure such as a procedure of avoiding an obstacle is initiated when a type A target is detected, so that the vehicle is automatically prevented from potential accidents.

Abstract: A scene analysis method is applied to a visual navigation device. The scene analysis method includes the steps of capturing scene information at different times based upon the field of view of an image capturing device; analyzing different targets existing in the captured scene information; comparing each one of the targets captured at different times to classify the target, wherein the target that is in a specific region in the field of view and moving toward the image capturing device is classified as a type A target; otherwise the target is classified as a type B target. When the visual navigation is installed on a vehicle, a protection procedure such as a procedure of avoiding an obstacle is initiated when a type A target is detected, so that the vehicle is automatically prevented from potential accidents.

Abstract: A scene analyzing method includes the steps of: receiving captured scene information; analyzing different targets in the scene information to obtain characteristic information of each of the targets; and sending the obtained characteristic information to an external device or correlating the characteristic information to the scene information and storing it to a storage device in order to retrieve the scene information corresponding to the characteristic information stored in the storage device. The scene information corresponding to the characteristic information is searchable to extract a specific target when the scene analyzing method is used for monitoring applications. This reduces human costs and increases efficiency in monitoring information.

Abstract: A method is provided for recognition of a sky portion, a vertical object portion and a ground portion in an image. The image into a plurality of pixel sets by the electronic system. Expected values of each pixel sets with a sky distribution function, a vertical object distribution function and a ground distribution function by the electronic system are calculated and compared for each pixel set for determine each pixel set belonging to one of the sky portion, the vertical object portion or the ground portion.

Abstract: A method is provided for recognition of a ceiling portion, a vertical object portion and a ground portion in an image of indoor scene executed in an electronic system. The image is divided into a plurality of pixel sets. Expected values of each pixel sets with a ceiling distribution function, a vertical object distribution function and a ground distribution function are calculated. The expected values of each pixel set in the ceiling distribution function, the vertical object distribution function and the ground distribution function are compared to determine whether each pixel set belongs to a ceiling object, a vertical object or a ground object.

Abstract: A method is provided for classifying pixels in an image into super pixels that may be executed in one or more than one electronic devices. Seed pixels are pixels selected from the image. Color distances of a color space similar to human vision perception between the seed pixels and proximal pixels are calculated. The proximal pixels are pixels located proximally to corresponding seed pixels. Geographic distances are also computed for two pixels and the geographic distances are combined with the color distances as a reference for classifying pixels into super pixels.

Abstract: Texture features of images are calculated for recognition of pixel sets in the images as one category among multiple candidate categories. For example, wavelet transformation is applied to obtain a wavelet vector. Via analyzing components of the wavelet vector, one pixel set may be recognized as part of an architecture object or a natural plant object. In addition, line segments within the pixel sets may be calculated and their statistics result may be used for recognizing different objects.

Abstract: A method is provided for recognition of a ceiling portion, a vertical object portion and a ground portion in an image of indoor scene executed in an electronic system. The image is divided into a plurality of pixel sets. Expected values of each pixel sets with a ceiling distribution function, a vertical object distribution function and a ground distribution function are calculated. The expected values of each pixel set in the ceiling distribution function, the vertical object distribution function and the ground distribution function are compared to determine whether each pixel set belongs to a ceiling object, a vertical object or a ground object.

Abstract: A method is provided for recognition of a sky portion, a vertical object portion and a ground portion in an image. The image into a plurality of pixel sets by the electronic system. Expected values of each pixel sets with a sky distribution function, a vertical object distribution function and a ground distribution function by the electronic system are calculated and compared for each pixel set for determine each pixel set belonging to one of the sky portion, the vertical object portion or the ground portion.

Abstract: Texture features of images are calculated for recognition of pixel sets in the images as one category among multiple candidate categories. For example, wavelet transformation is applied to obtain a wavelet vector. Via analyzing components of the wavelet vector, one pixel set may be recognized as part of an architecture object or a natural plant object. In addition, line segments within the pixel sets may be calculated and their statistics result may be used for recognizing different objects.

Abstract: A method is provided for classifying pixels in an image into super pixels that may be executed in one or more than one electronic devices. Seed pixels are pixels selected from the image. Color distances of a color space similar to human vision perception between the seed pixels and proximal pixels are calculated. The proximal pixels are pixels located proximally to corresponding seed pixels. Geographic distances are also computed for two pixels and the geographic distances are combined with the color distances as a reference for classifying pixels into super pixels.