diff --git a/pm2d/line_matcher.py b/pm2d/line_matcher.py
index a7fcb3c..4a4f83d 100644
--- a/pm2d/line_matcher.py
+++ b/pm2d/line_matcher.py
@@ -494,19 +494,25 @@ class LineShapeMatcher:
             nms_radius = max(8, min(self.template_size) // 2)
         top_thresh = min_score * self.top_score_factor
 
-        # Background score LOCALE: densità media bin-attivi normalizzata su
-        # bbox template. Rinormalizzazione rimuove match dove la zona ha
-        # attivazioni dense in tutti gli orientamenti (texture/rumore).
+        # Background map PER-SCALA: densità media bin attivi normalizzata
+        # su bbox template scalata. Rinormalizza score per isolare contributo
+        # non-random e riduce FP in zone con attivazione densa.
         tw, th = self.template_size
+        density_top = resp_top.sum(axis=0)
+        sf_top = 2 ** top
+        bg_cache_top: dict[float, np.ndarray] = {}
+        bg_cache_full: dict[float, np.ndarray] = {}
+        unique_scales = sorted({var.scale for var in self.variants})
 
-        def _bg_map(resp: np.ndarray, scale_div: int = 1) -> np.ndarray:
-            """bg_map[y,x] = frazione bin attivi media in bbox template."""
-            density = resp.sum(axis=0)  # (H, W)
-            bw = max(9, tw // scale_div); bh = max(9, th // scale_div)
-            smooth = cv2.boxFilter(density, cv2.CV_32F, (bw, bh))
-            return np.clip(smooth / N_BINS, 0.0, 0.99)
+        def _bg_for_scale(density: np.ndarray, scale: float,
+                          divisor: int) -> np.ndarray:
+            bw = max(9, int(round(tw * scale / divisor)))
+            bh = max(9, int(round(th * scale / divisor)))
+            sm = cv2.boxFilter(density, cv2.CV_32F, (bw, bh))
+            return np.clip(sm / N_BINS, 0.0, 0.99)
 
-        bg_top = _bg_map(resp_top, scale_div=2 ** top)
+        for sc in unique_scales:
+            bg_cache_top[sc] = _bg_for_scale(density_top, sc, sf_top)
 
         def _rescore(score: np.ndarray, bg: np.ndarray) -> np.ndarray:
             return np.maximum(0.0, (score - bg) / (1.0 - bg + 1e-6))
@@ -518,7 +524,7 @@ class LineShapeMatcher:
             score = self._score_by_shift(
                 resp_top, lvl.dx, lvl.dy, lvl.bin, bin_has_data=bin_has_top,
             )
-            score = _rescore(score, bg_top)
+            score = _rescore(score, bg_cache_top[var.scale])
             return vi, float(score.max()) if score.size else -1.0
 
         kept_variants: list[tuple[int, float]] = []
@@ -536,14 +542,16 @@ class LineShapeMatcher:
         if not kept_variants:
             return []
 
-        max_vars_full = max(8, max_matches * 4)
+        # Cap: tutte le varianti che superano top_thresh passano al full-res.
+        # Ordinamento per score decrescente (early matches hanno priorità).
         kept_variants.sort(key=lambda t: -t[1])
-        kept_variants = kept_variants[:max_vars_full]
 
         # Full-res (parallelizzato per variante)
         resp0 = self._response_map(gray0)
         bin_has_full = np.array([resp0[b].any() for b in range(N_BINS)])
-        bg_full = _bg_map(resp0, scale_div=1)
+        density_full = resp0.sum(axis=0)
+        for sc in unique_scales:
+            bg_cache_full[sc] = _bg_for_scale(density_full, sc, 1)
 
         def _full_score(vi: int) -> tuple[int, np.ndarray]:
             var = self.variants[vi]
@@ -551,7 +559,7 @@ class LineShapeMatcher:
             score = self._score_by_shift(
                 resp0, lvl0.dx, lvl0.dy, lvl0.bin, bin_has_data=bin_has_full,
             )
-            score = _rescore(score, bg_full)
+            score = _rescore(score, bg_cache_full[var.scale])
             return vi, score
 
         candidates_per_var: list[tuple[int, np.ndarray]] = []