Calculates the back projection of a histogram

backProject = cv.calcBackProject(images, H, ranges)
backProject = cv.calcBackProject(..., 'OptionName',optionValue, ...)

Input

• images Source arrays. A numeric array, or cell array of numeric arrays are accepted. They all should have the same class (uint8, uint16, or single) and the same row/column size. Each of them can have an arbitrary number of channels.
• H Input histogram that can be dense or sparse.
• ranges Cell-array of arrays of the histogram bin boundaries in each dimension. See cv.calcHist.

Output

• backProject Destination back projection array that is a single-channel array of the same row/column size and depth as images (if numeric) or images{1} (if images is a cell array). Out of uint8, uint16, or single classes. Pay attention to the data type of the back projection array, where integer types are clamped to the maximum intmax if a value exceeds the largest possible integer of the type.

Options

• Channels The list of channels used to compute the back projection (as 0-based indices). The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to size(images{1},3)-1, the second array channels are counted from size(images{1},3) to size(images{1},3) + size(images{2},3)-1, and so on. By default, all channels from all images are used, i.e default is 0:sum(cellfun(@(im)size(im,3), images))-1 when input images is a cell array, and 0:(size(images,3)-1) when input images is a numeric array.
• Uniform Logical flag indicating whether the histogram is uniform or not (see above). default false.
• Scale Optional scale factor for the output back projection. default 1

The function cv.calcBackProject calculates the back project of the histogram. That is, similarly to cv.calcHist, at each location (x, y) the function collects the values from the selected channels in the input images and finds the corresponding histogram bin. But instead of incrementing it, the function reads the bin value, scales it by scale, and stores in backProject(x,y). In terms of statistics, the function computes probability of each element value in respect with the empirical probability distribution represented by the histogram. See how, for example, you can find and track a bright-colored object in a scene:

1. Before tracking, show the object to the camera so that it covers almost the whole frame. Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant colors in the object.
2. When tracking, calculate a back projection of a hue plane of each input video frame using that pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels.
3. Find connected components in the resulting picture and choose, for example, the largest component.

This is an approximate algorithm of the cv.CamShift color object tracker.