Abstract: An image analysis method is applied to a camera apparatus and used to analyze the landform inside a detection image captured by the camera apparatus via a predefined installation angle. The image analysis method includes dividing the detection image into a plurality of first regions to acquire a plurality of first height, defining some first regions with the same height and adjacent to each other as a first reference plane area to acquire a first plane formula, selecting at least one first region adjacent to and outside the first reference plane area, applying the selected first region for the first plane formula to acquire a first planar height and comparing the first planar height with an initial first height of the at least one first region, and determining landform between the first reference plane area and the selected at least one first region in accordance with a comparison result.

Abstract: An image information transformation method is applied to an image analysis device with an operation processor and includes generating a false object datum in a predefined manner, and deciding whether to output the false object datum in accordance with an identification result of a user license.

Abstract: An image defect identification method is applied to an image analysis device with an image receiver and an operation processor. The image defect identification method divides a detection image acquired by the image receiver into a plurality of pixel groups, transforms one of the plurality of pixel groups into a distribution curve, compares the distribution curve with a reference curve, and determines an area of the detection image conforming to a specific section of the distribution curve has defect when a difference between the specific section of the distribution curve and a related section of the reference curve is greater than a predefined threshold.

Abstract: An image calibration method applied to an image calibration device includes comparing a first image with a second image to acquire a first overlapping region of the first image and a second overlapping region of the second image, analyzing color distribution of the first overlapping region to acquire at least one first base color value, analyzing color distribution of the second overlapping region to acquire at least one second base color value, setting a ratio of the at least one first base color value to the at least one second base color value as an luminance compensation value when the at least one first base color value is greater than the at least one second base color value, and utilizing the luminance compensation value to adjust pixels of the second image. The first overlapping region is overlapped with the second overlapping region.

Abstract: An object counting method is applied to a surveillance camera and used to determine an amount and a direction an object passing over a surveillance region. The object counting method includes acquiring a plurality of known traces, dividing the plurality of known traces into a first group passing the surveillance region along a first direction and a second group passing the surveillance region along a second direction, computing a first start point computed value of the first group and a second start point computed value of the second group to acquire a start point connection vector, and comparing the start point connection vector with a trace vector of a target object to determine whether the target object passes over the surveillance region along the first direction or the second direction.

Abstract: An image restoration method is applied to an image restoration device and includes acquiring an analyzing image containing a region of interest and a background region, utilizing a first restoration function to calibrate the region of interest and generate a calibrated region of interest, and combining the calibrated region of interest with the background region via a relative position between the region of interest and the background region for providing a restoration image.

Abstract: An automatic feeding device includes an accommodating container, a transmission channel, a valve unit, a sensor and an ejection unit. The transmission channel is disposed on bottom of the accommodating container. The transmission channel has an inlet portion, a central portion and an outlet portion. The central portion is between the inlet portion and the outlet portion. The valve unit is disposed on the bottom of the accommodating container and adjacent to the inlet portion. The valve unit is switchable to allow feed inside the accommodating container for passing the inlet portion and entering the transmission channel. The sensor is disposed on the inlet portion and detects whether the feed passes the inlet portion, and further outputs a control command to switch modes of the valve unit. The ejection unit is disposed on the central portion and used to push the feed inside the transmission channel toward the outlet portion.

Abstract: An image analysis model establishment method is applied to an image analysis apparatus including an imager receiver and an operation processor. The image analysis model establishment method divides a detection image acquired by the imager receiver into a plurality of sub-images and transforms the sub-images from a spatial domain to a frequency domain to generate a plurality of pre-processing frequency domain data, generating an analysis model output layer by an inner product of the plurality of pre-processing frequency domain data transformed via a plurality of masks and several filters through a fully connected multilayer perceptron network, acquiring a predicted result of the detection image in accordance with a category determination result of the analysis model output layer, and comparing the predicted result with a target label to adjust parameters in each transformation phase applied for the plurality of pre-processing frequency domain data in accordance with a comparison result.

Abstract: An object classifying and tracking method is applied to an image stream acquired by a surveillance camera for object identification. The object classifying and tracking method includes acquiring a first classify feature and a first dependent feature of a first target object in a first image of the image stream and a second classify feature and a second dependent feature of a second target object in a second image of the image stream, acquiring a dependent degree of the first classify feature and the second classify feature from a memory, transforming the second dependent feature via the dependent degree, and analyzing the first dependent feature and the transformed second dependent feature to determine whether the first target object and the second target object are the same object.

Abstract: A tension adjustment mechanism applied to a surveillance camera and includes a base, a positioning component, a first actuating component and a first fixing component. The base includes a first slot and first hole. The positioning component is slidably disposed on the base. The first actuating component has a first fixed end and a first free end opposite to each other. The first fixed end is fixedly disposed on the positioning component. The first free end has an inclined guiding structure and is movably inserted into the first slot. The first fixing component abuts against the inclined guiding structure and is movably locked with the first hole.

Abstract: An image identification method is applied to an image surveillance apparatus and includes utilizing an object detecting function to generate a first bounding box and a second bounding box within a surveillance image, utilizing a foreground detecting function to generate a plurality of markers within the surveillance image, defining some of the plurality of markers which conform to the first bounding box as a first marker group and further defining other markers which do not conform to the first bounding box as a second marker group, determining whether the second marker group conforms to the second bounding box, and deciding whether the second bounding box belongs to an error of the object detecting function in accordance with a determination result.

Abstract: An image analyzing method of increasing analysis accuracy is applied to an image monitoring apparatus with a processor. The image analyzing method is executed by the first processor and includes receiving statistic data of an external electronic unit, transforming the statistic data into feedback data via specific algorithm, and generating a control parameter according to the feedback data. The control parameter is used to optimize image data output by the image monitoring apparatus.

Abstract: An image processing method is applied to a camera device and a camera system. The image processing method includes dividing each image frame of a video film into a plurality of unit regions, computing a plurality of data quantity respectively corresponding to the plurality of unit regions, compiling statistics of a total data quantity of each unit region within a plurality of image frames of the video film, and distinguishing the each unit region from the other unit regions in accordance with the total data quantity.

Abstract: An object identification method determines whether a first monitoring image and a second monitoring image captured by a monitoring camera apparatus have the same object. The object identification method includes acquiring the first monitoring image at a first point of time to analyze a first object inside a first angle of view of the first monitoring image, acquiring the second monitoring image at a second point of the time different from the first point of time to analyze a second object inside the first angle of view of the second monitoring image, estimating a first similarity between the first object inside the first angle of view of the first monitoring image and the second object inside the first angle of view of the second monitoring image; and determining whether the first object and the second object are the same according to comparison result of the first similarity with a threshold.