boofcv.alg.feature.associate

Class DescriptorDistance

java.lang.Object

boofcv.alg.feature.associate.DescriptorDistance

public class DescriptorDistance
extends java.lang.Object

Series of simple functions for computing difference distance measures between two descriptors.

Constructor Summary

Constructors

Constructor and Description
DescriptorDistance()

Method Summary

Methods

Modifier and Type	Method and Description
static double	correlation(TupleDesc_F64 a, TupleDesc_F64 b) Correlation score
static double	euclidean(TupleDesc_F64 a, TupleDesc_F64 b) Returns the Euclidean distance (L2-norm) between the two descriptors.
static double	euclideanSq(TupleDesc_F32 a, TupleDesc_F32 b) Returns the Euclidean distance squared between the two descriptors.
static double	euclideanSq(TupleDesc_F64 a, TupleDesc_F64 b) Returns the Euclidean distance squared between the two descriptors.
static int	hamming(int val) Computes the hamming distance.
static int	hamming(TupleDesc_B a, TupleDesc_B b) Computes the hamming distance between two binary feature descriptors
static double	ncc(NccFeature a, NccFeature b) Normalized cross correlation (NCC) computed using a faster technique. NCC = sum(a[i]*b[i]) / (N*sigma_a * sigma_b) where a[i] = I[i]-mean(a), I[i] is the image pixel intensity around the feature, and N is the number of elements.
static float	sad(TupleDesc_F32 a, TupleDesc_F32 b) Sum of absolute difference (SAD) score
static double	sad(TupleDesc_F64 a, TupleDesc_F64 b) Sum of absolute difference (SAD) score
static int	sad(TupleDesc_S8 a, TupleDesc_S8 b) Sum of absolute difference (SAD) score
static int	sad(TupleDesc_U8 a, TupleDesc_U8 b) Sum of absolute difference (SAD) score

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

DescriptorDistance

public DescriptorDistance()

Method Detail

euclidean

public static double euclidean(TupleDesc_F64 a,
               TupleDesc_F64 b)

Returns the Euclidean distance (L2-norm) between the two descriptors.

Parameters:

a - First descriptor

b - Second descriptor

Returns:

Euclidean distance

euclideanSq

public static double euclideanSq(TupleDesc_F64 a,
                 TupleDesc_F64 b)

Returns the Euclidean distance squared between the two descriptors.

Parameters:

a - First descriptor

b - Second descriptor

Returns:

Euclidean distance squared

euclideanSq

public static double euclideanSq(TupleDesc_F32 a,
                 TupleDesc_F32 b)

Returns the Euclidean distance squared between the two descriptors.

Parameters:

a - First descriptor

b - Second descriptor

Returns:

Euclidean distance squared

correlation

public static double correlation(TupleDesc_F64 a,
                 TupleDesc_F64 b)

Correlation score

Parameters:

a - First descriptor

b - Second descriptor

Returns:

Correlation score

ncc

public static double ncc(NccFeature a,
         NccFeature b)

Normalized cross correlation (NCC) computed using a faster technique.

NCC = sum(a[i]*b[i]) / (N*sigma_a * sigma_b)
where a[i] = I[i]-mean(a), I[i] is the image pixel intensity around the feature, and N is the number of elements.

Parameters:

a - First descriptor

b - Second descriptor

Returns:

NCC score

sad

public static int sad(TupleDesc_U8 a,
      TupleDesc_U8 b)

Sum of absolute difference (SAD) score

Parameters:

a - First descriptor

b - Second descriptor

Returns:

SAD score

sad

public static int sad(TupleDesc_S8 a,
      TupleDesc_S8 b)

Sum of absolute difference (SAD) score

Parameters:

a - First descriptor

b - Second descriptor

Returns:

SAD score

sad

public static float sad(TupleDesc_F32 a,
        TupleDesc_F32 b)

Sum of absolute difference (SAD) score

Parameters:

a - First descriptor

b - Second descriptor

Returns:

SAD score

sad

public static double sad(TupleDesc_F64 a,
         TupleDesc_F64 b)

Sum of absolute difference (SAD) score

Parameters:

a - First descriptor

b - Second descriptor

Returns:

SAD score

hamming

public static int hamming(TupleDesc_B a,
          TupleDesc_B b)

Computes the hamming distance between two binary feature descriptors

Parameters:

a - First variable

b - Second variable

Returns:

The hamming distance

hamming

public static int hamming(int val)

Computes the hamming distance. A bit = 0 is a match and 1 is not match

Parameters:

val - Hamming encoding

Returns:

The hamming distance