Program for Extracting Image Shape Features Using Fourier Band Brightness Descriptors

Fourier Band Brightness Descriptors serve as a powerful tool for extracting shape features from images. This method transforms spatial domain shape information into more descriptive frequency domain features through spectral analysis. The technique finds wide applications in computer vision, particularly in object recognition and classification tasks.

The core algorithmic concept involves converting image contours from the spatial domain to the frequency domain. The coefficients obtained after Fourier transformation effectively characterize shape features. Compared to direct spatial domain processing, frequency domain representation offers advantages of rotation, translation, and scale invariance, making feature descriptions more stable and reliable. In code implementation, this typically involves using FFT (Fast Fourier Transform) algorithms for efficient computation.

The implementation process begins with acquiring contour information of target objects, followed by converting these contour points into complex number representations. The discrete Fourier transform is then applied to the complex sequence to obtain frequency domain coefficients. The low-frequency components in these coefficients contain primary shape characteristics, while high-frequency components reflect detailed features. A typical MATLAB implementation would use contour tracing functions like bwboundaries() followed by complex number conversion using complex(real_part, imag_part).

The program typically retains the first N low-frequency coefficients as shape descriptors. This truncation operation reduces computational complexity while preserving essential shape characteristics. To enhance feature robustness, coefficient normalization is commonly performed to eliminate scale variation effects. Code implementation often includes steps like coefficient magnitude calculation using abs() function and normalization through division by the DC component.

In practical applications, this shape description method effectively handles various shape variations, including slight deformations and occlusions. By comparing Fourier descriptors of different images, shape similarity calculation and classification recognition can be achieved through distance metrics like Euclidean distance between descriptor vectors.